Empathy

A five-level maturity model for AI-human collaboration

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Quick Start

# Install core framework
pip install empathy-framework

# Install with MemDocs + LLM providers (recommended)
# Built with Claude Code - created in consultation with Claude Sonnet 4.5
pip install empathy-framework[full]

# Or install specific components:
pip install empathy-framework[llm]      # LLM providers (Anthropic, OpenAI)
pip install empathy-framework[memdocs]  # MemDocs integration
pip install empathy-framework[all]      # Everything including dev tools

Development installation:

git clone https://github.com/Smart-AI-Memory/empathy-framework.git
cd empathy-framework
pip install -e .[dev]

from empathy_os import EmpathyOS

os = EmpathyOS()
result = await os.collaborate("Build a secure API endpoint")

📖 Full Quick Start Guide | API Reference | User Guide

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Overview

The Empathy is a systematic approach to building AI systems that progress from reactive responses to anticipatory problem prevention. By integrating emotional intelligence (Goleman), tactical empathy (Voss), systems thinking (Meadows, Senge), and clear reasoning (Naval Ravikant), it provides a maturity model for AI-human collaboration.

Part of the Smart-AI-Memory ecosystem - Designed to work seamlessly with MemDocs for intelligent document memory and retrieval, enabling AI systems to maintain long-term context and learn from interactions over time.

🚀 Built with Claude Code

"Transformation occurs when structure meets collaboration."

From The Empathy Framework book chapter - Well-defined roles, clear processes, and explicit frameworks enable any system to transcend linear growth.

This framework was created in consultation with Claude Sonnet 4.5 using Claude Code (Anthropic's official developer tool - CLI and VS Code extension) combined with MemDocs and the Empathy Framework itself, demonstrating the 200-400% productivity gains possible with Level 4 Anticipatory AI:

Claude Code + MemDocs + Empathy Framework = Level 4-5 Development

• Claude Code: Anthropic's official developer tool - AI pair programming with multi-file editing, command execution, and anticipatory assistance (CLI and VS Code extension)
• MemDocs: Git-native project memory maintaining context across sessions (also created with Claude Code)
• Empathy Framework: Structured 5-level maturity model guiding AI behavior from reactive to anticipatory

Measured Results from This Project:

• 32.19% → 83.13% test coverage in systematic phases (2.6x increase)
• 887 → 1,247 tests added (+360 comprehensive tests)
• 24 files at 100% coverage (vs. 0 at start)
• Parallel agent processing completing 3 complex modules simultaneously
• Zero test failures maintained throughout (quality at scale)

The combination of Claude Code (Anthropic's official developer tool) providing Level 4 anticipatory suggestions, MemDocs maintaining architectural context, and the Empathy Framework ensuring systematic quality progression demonstrates the non-linear productivity multiplier effect described in our book chapter.

Both empathy-framework and memdocs were created in consultation with Claude Sonnet 4.5 using Claude Code, showcasing what's possible when developer tools reach Level 4-5 maturity.

Empathy, in this context, is not about feelings—it's about:

• Alignment: Understanding the human's goals, context, and constraints
• Prediction: Anticipating future needs based on system trajectory
• Timely Action: Intervening at the right moment with the right support
• Memory Integration: Leveraging MemDocs to maintain context across sessions and learn from patterns

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What the Empathy Provides

A systematic approach to building AI systems with five levels of maturity:

Included Components:

• 3 canonical wizard examples - Healthcare, Customer Support, and Technology domains
• LLM toolkit - Integration with Claude, GPT-4, and other models
• Plugin system - Extensible architecture for custom domains
• FastAPI backend - REST API for analysis and orchestration
• Pre-commit hooks - Automated code quality checks

Additional Wizards (External Packages):

• Healthcare wizards (23) - Live Dashboard
• Tech & AI wizards (16) - Live Dashboard
• Business wizards - Coming soon as separate packages

Framework Philosophy:

• Five-level maturity model: Reactive (1) → Guided (2) → Proactive (3) → Anticipatory (4) → Systems (5)
• Context-aware AI assistance at each level
• Pattern-based development and sharing
• Systems thinking integration (Meadows, Senge)
• Tactical empathy (Voss) and clear reasoning (Ravikant)

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Current Capabilities

The Empathy provides:

Security & Code Quality:

• Pre-commit hooks for automated quality checks
• Security pattern detection (SQL injection, XSS, CSRF)
• Performance anti-pattern identification
• Extensible wizard base class for custom implementations

AI Integration:

• LLM toolkit with Claude Sonnet 4.5 and GPT-4 support
• Async API calls with prompt caching
• Thinking mode for complex reasoning
• Multi-model fallback support

Domain Examples:

• 3 canonical wizard implementations (Healthcare, Customer Support, Technology)
• HIPAA-compliant healthcare patterns
• Full wizard collections available via external dashboards and packages

Developer Experience:

• Works on single files or entire projects
• FastAPI backend for REST API access
• Plugin architecture for extensibility
• Pattern library for Level 5 sharing

Development Status

Production Ready (Beta → Stable):

• ✅ Core framework architecture (100% coverage on critical modules)
• ✅ LLM toolkit and provider integrations (100% coverage)
• ✅ Canonical wizard examples (Healthcare, Customer Support, Technology)
• ✅ Plugin architecture for extensibility
• ✅ Pre-commit hooks and quality tools (black, ruff, bandit)
• ✅ 1,247 tests passing (83.13% overall coverage) ← Up from 553 tests (63.87%)
• ✅ Multi-platform support (Linux, macOS, Windows)

Test Coverage Details (Updated Jan 2025):

• 24 files at 100% coverage ✅
• empathy_os/core.py: 100% ✅
• empathy_os/persistence.py: 100% ✅
• empathy_llm_toolkit/core.py: 100% ✅
• empathy_llm_toolkit/levels.py: 100% ✅
• empathy_software_plugin/plugin.py: 95.71% ✅
• Healthcare trajectory analyzer: 95.88% ✅
• Config and state management: 98%+ ✅

Quality Achievements:

• 360 comprehensive tests added in systematic phases
• Zero test failures maintained throughout coverage push
• Parallel agent processing validated at scale
• OpenSSF Best Practices preparation in progress

Next Milestones:

• ⚙️ 90% coverage target (only 6.87% gap remaining)
• ⚙️ OpenSSF Best Practices Badge certification
• ⚙️ Production/Stable status declaration
• ⚙️ PyPI package v2.0 release

Try It Yourself

# Clone and install
git clone https://github.com/Smart-AI-Memory/empathy-framework.git
cd empathy-framework
pip install -r requirements.txt

# Set up API key
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

# Run tests (non-LLM tests don't need API key)
pytest -m "not llm"

# Run full test suite (requires API key)
pytest

Share your experience in GitHub Discussions.

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The Five Levels

Level	Name	Core Behavior	Timing	Example
1	Reactive	Help after being asked	Lagging	"You asked for data, here it is"
2	Guided	Collaborative exploration	Real-time	"Let me ask clarifying questions"
3	Proactive	Act before being asked	Leading	"I pre-fetched what you usually need"
4	Anticipatory	Predict future needs	Predictive	"Next week's audit is coming—docs ready"
5	Systems	Build structures that help at scale	Structural	"I designed a framework for all future cases"

Progression Pattern

Level 1: Reactive
    ↓ (Add context awareness)
Level 2: Guided
    ↓ (Add pattern detection)
Level 3: Proactive
    ↓ (Add trajectory prediction)
Level 4: Anticipatory
    ↓ (Add structural design)
Level 5: Systems

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Quick Start Options

Option 1: One-Command Install

curl -sSL https://raw.githubusercontent.com/Smart-AI-Memory/empathy/main/install.sh | bash

Then scan your code:

empathy-scan security app.py          # Scan one file for security issues
empathy-scan performance ./src        # Scan directory for performance issues
empathy-scan all ./project            # Run all checks on entire project

Option 2: Docker (Zero Install)

# Security scan
docker run -v $(pwd):/code ghcr.io/smart-ai-memory/empathy-scanner security /code

# Performance scan
docker run -v $(pwd):/code ghcr.io/smart-ai-memory/empathy-scanner performance /code

# Full scan
docker run -v $(pwd):/code ghcr.io/smart-ai-memory/empathy-scanner all /code

Option 3: Pre-commit Hook (Automatic Scanning)

# Copy the example pre-commit config
cp .pre-commit-config.example.yaml .pre-commit-hooks.yaml

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Now security scans run on every commit, performance scans on every push!

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Quick Start

Installation

# Clone the repository
git clone https://github.com/Smart-AI-Memory/empathy-framework.git
cd empathy-framework

# Install dependencies
pip install -r requirements.txt

Basic Usage

For Software Developers (Coach Wizards):

from coach_wizards import SecurityWizard, PerformanceWizard

# Initialize wizards
security = SecurityWizard()
performance = PerformanceWizard()

# Analyze code for security issues
code = """
def login(username, password):
    query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
    return db.execute(query)
"""

security_result = security.run_full_analysis(
    code=code,
    file_path="app.py",
    language="python",
    project_context={
        "team_size": 10,
        "deployment_frequency": "daily"
    }
)

print(f"Security analysis: {security_result.summary}")
print(f"Current issues: {len(security_result.issues)}")
print(f"Predicted issues (90 days): {len(security_result.predictions)}")

# See examples/ for complete implementations

For Healthcare (Clinical Agents):

from agents.compliance_anticipation_agent import ComplianceAnticipationAgent

# Initialize Level 4 Anticipatory agent
agent = ComplianceAnticipationAgent()

# Predict future compliance needs
result = agent.predict_audit(
    context="Healthcare facility with 500 patient records",
    timeline_days=90
)

print(f"Predicted audit date: {result.predicted_date}")
print(f"Compliance gaps: {result.gaps}")
print(f"Recommended actions: {result.actions}")

---

Repository Structure

Empathy/
├── agents/                          # Level 4 Anticipatory agents (3 files)
│   ├── compliance_anticipation_agent.py  # 90-day audit prediction
│   ├── trust_building_behaviors.py       # Tactical empathy patterns
│   └── epic_integration_wizard.py        # Healthcare EHR integration
├── coach_wizards/                   # Software development wizards (16 files + base)
│   ├── base_wizard.py              # Base wizard implementation
│   ├── security_wizard.py          # Security vulnerabilities
│   ├── performance_wizard.py       # Performance optimization
│   ├── accessibility_wizard.py     # WCAG compliance
│   ├── testing_wizard.py           # Test coverage & quality
│   ├── refactoring_wizard.py       # Code quality
│   ├── database_wizard.py          # Database optimization
│   ├── api_wizard.py               # API design
│   ├── debugging_wizard.py         # Error detection
│   ├── scaling_wizard.py           # Scalability analysis
│   ├── observability_wizard.py     # Logging & metrics
│   ├── cicd_wizard.py              # CI/CD pipelines
│   ├── documentation_wizard.py     # Documentation quality
│   ├── compliance_wizard.py        # Regulatory compliance
│   ├── migration_wizard.py         # Code migration
│   ├── monitoring_wizard.py        # System monitoring
│   └── localization_wizard.py      # Internationalization
├── empathy_llm_toolkit/             # Core LLM toolkit
│   └── wizards/                    # Canonical wizard examples (4 files)
│       ├── base_wizard.py          # Base wizard class
│       ├── healthcare_wizard.py    # Healthcare domain example
│       ├── customer_support_wizard.py  # Business domain example
│       └── technology_wizard.py    # Software/tech domain example
├── services/                        # Core services
│   └── wizard_ai_service.py        # Wizard orchestration service
├── docs/                            # Framework documentation (8 files)
│   ├── CHAPTER_EMPATHY_FRAMEWORK.md
│   ├── EMPATHY_FRAMEWORK_NON_TECHNICAL_GUIDE.md
│   ├── TEACHING_AI_YOUR_PHILOSOPHY.md
│   └── 5 more documentation files...
├── examples/                        # Implementation examples
│   └── coach/                      # Coach IDE integration (87 files)
│       ├── jetbrains-plugin-complete/  # IntelliJ IDEA plugin
│       ├── vscode-extension-complete/  # VS Code extension
│       └── coach-lsp-server/          # LSP server
├── tests/                           # Test suite
├── LICENSE                          # Fair Source 0.9
├── README.md                        # This file
└── requirements.txt                 # Python dependencies

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Key Components

1. Anticipatory Agents

Compliance Anticipation Agent (compliance_anticipation_agent.py)

• Predicts regulatory audits 90+ days in advance
• Identifies compliance gaps automatically
• Generates proactive documentation
• Provides stakeholder notifications

Trust Building Behaviors (trust_building_behaviors.py)

• Implements tactical empathy patterns
• Builds human-AI trust through transparent communication
• Uses calibrated questions to uncover hidden needs

EPIC Integration Wizard (epic_integration_wizard.py)

• Healthcare-specific implementation
• Integrates with EPIC EHR systems
• Level 4 anticipatory empathy for clinical workflows

2. Coach Software Development Wizards

16 specialized wizards for software development with Level 4 Anticipatory Empathy:

Security & Compliance:

• Security Wizard (security_wizard.py) - SQL injection, XSS, CSRF, secrets detection
• Compliance Wizard (compliance_wizard.py) - GDPR, SOC 2, PII handling

Performance & Scalability:

• Performance Wizard (performance_wizard.py) - N+1 queries, memory leaks, bottlenecks
• Database Wizard (database_wizard.py) - Missing indexes, query optimization
• Scaling Wizard (scaling_wizard.py) - Architecture limitations, load handling

Code Quality:

• Refactoring Wizard (refactoring_wizard.py) - Code smells, complexity, duplication
• Testing Wizard (testing_wizard.py) - Coverage analysis, flaky tests
• Debugging Wizard (debugging_wizard.py) - Null references, race conditions

API & Integration:

• API Wizard (api_wizard.py) - Design consistency, versioning
• Migration Wizard (migration_wizard.py) - Deprecated APIs, compatibility

DevOps & Operations:

• CI/CD Wizard (cicd_wizard.py) - Pipeline optimization, deployment risks
• Observability Wizard (observability_wizard.py) - Logging, metrics, tracing
• Monitoring Wizard (monitoring_wizard.py) - Alerts, SLOs, blind spots

User Experience:

• Accessibility Wizard (accessibility_wizard.py) - WCAG compliance, alt text, ARIA
• Localization Wizard (localization_wizard.py) - i18n, translations, RTL

Documentation:

• Documentation Wizard (documentation_wizard.py) - Docstrings, examples, clarity

Each wizard implements:

• Current Analysis: Detect issues in code now
• Level 4 Predictions: Forecast issues 30-90 days ahead
• Prevention Strategies: Stop problems before they happen
• Fix Suggestions: Concrete code examples

3. Canonical Wizard Examples

3 domain examples demonstrating the framework's capabilities:

Included in Core Framework:

• Healthcare Wizard (healthcare_wizard.py) - HIPAA-compliant medical assistant
• Customer Support Wizard (customer_support_wizard.py) - Customer service and help desk
• Technology Wizard (technology_wizard.py) - IT and software development operations

Additional Specialized Wizards:

For comprehensive domain-specific wizards, visit:

• Healthcare (23 wizards) - Live Dashboard - SBAR, SOAP notes, patient assessment, medication safety, and more
• Tech & AI (16 wizards) - Live Dashboard - Debugging, testing, security, performance, AI collaboration
• Business wizards - Coming soon as separate packages (pip install empathy-healthcare-wizards, empathy-software-wizards)

4. Core Services

Wizard AI Service (wizard_ai_service.py)

• Orchestrates wizard implementations
• Manages AI model selection and fallback
• Handles prompt templates and context
• Integrates with Claude, GPT-4, and other LLMs

5. Framework Documentation

Technical Guide (CHAPTER_EMPATHY_FRAMEWORK.md)

• Complete theoretical foundation
• Implementation patterns
• Code examples for each level
• Systems thinking integration

Non-Technical Guide (EMPATHY_FRAMEWORK_NON_TECHNICAL_GUIDE.md)

• Accessible explanation for stakeholders
• Business value and ROI
• Real-world use cases

Teaching AI Your Philosophy (TEACHING_AI_YOUR_PHILOSOPHY.md)

• How to align AI systems with your values
• Collaborative prompt engineering
• Building long-term AI partnerships

6. Coach Integration Examples

The Coach project demonstrates practical implementation of Level 4 Anticipatory Empathy in IDE integrations:

• JetBrains Plugin: Complete IntelliJ IDEA plugin with 16 specialized wizards
• VS Code Extension: Full-featured extension with real-time analysis
• LSP Server: Language Server Protocol implementation for cross-IDE support

See examples/coach/ for complete implementations.

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Real-World Applications

Healthcare: AI Nurse Florence

• Level 4 Anticipatory: Predicts patient deterioration 30-90 days ahead
• Compliance: Auto-generates audit documentation
• Clinical Decision Support: Proactive alerts based on trajectory analysis
• Repository: https://github.com/Deep-Study-AI/ai-nurse-florence

Software Development: Coach IDE Extensions

• Level 4 Anticipatory: Predicts code issues before they manifest
• Security: Identifies vulnerabilities in development phase
• Performance: Detects N+1 queries and scalability issues early
• 16 Specialized Wizards: Security, Performance, Accessibility, Testing, etc.
• Examples: See examples/coach/

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Featured Example: Level 5 Transformative Empathy

Healthcare Handoff Patterns → Software Deployment Safety

This example demonstrates the Empathy Framework's unique Level 5 Systems Empathy capability—learning patterns in one domain (healthcare) and applying them to prevent failures in another domain (software deployment).

The Cross-Domain Pattern Transfer

The Problem: Both hospital patient handoffs (nurse shift changes, patient transfers) and software deployment handoffs (dev → staging → production) share identical failure modes:

• Critical information loss during transitions
• Lack of explicit verification steps
• Assumptions about what the receiving party knows
• Time pressure leading to shortcuts

The Solution: Healthcare research found that 23% of handoffs fail without verification checklists. The Empathy Framework learns this pattern from healthcare code and applies it to predict deployment failures with 87% confidence.

What Makes This Unique

No other AI framework can do this.

Traditional AI tools analyze code in isolation within a single domain. The Empathy Framework with MemDocs integration:

1. Analyzes healthcare handoff protocols (ComplianceWizard)
2. Extracts and stores the "critical handoff failure" pattern in long-term memory
3. Analyzes software deployment code (CICDWizard)
4. Retrieves the healthcare pattern via cross-domain matching
5. Predicts deployment failures 30-45 days ahead
6. Recommends prevention steps derived from healthcare best practices

Run the Demo

# Install with MemDocs integration
pip install empathy-framework[full]

# Run the Level 5 demo
python examples/level_5_transformative/run_full_demo.py

Output preview:

=== STEP 1: Healthcare Domain Analysis ===
ComplianceWizard Analysis:
  🔴 [ERROR] Critical handoff without verification checklist
  ✓ Pattern 'critical_handoff_failure' stored in memory
  ℹ️  Key finding: Handoffs without verification fail 23% of the time

=== STEP 2: Software Domain Analysis ===
CROSS-DOMAIN PATTERN DETECTION
✓ Pattern match found from healthcare domain!

⚠️  DEPLOYMENT HANDOFF FAILURE PREDICTED
  📅 Timeframe: 30-45 days
  🎯 Confidence: 87%
  💥 Impact: HIGH

PREVENTION STEPS:
  1. Create deployment checklist (mirror healthcare approach)
  2. Require explicit sign-off between staging and production
  3. Implement automated handoff verification
  4. Add read-back confirmation for critical environment variables
  5. Document rollback procedure as part of handoff

Real-World Impact

• Healthcare: Joint Commission found 80% of medical errors occur during handoffs
• Software: Deployment failures often trace to missing handoff verification
• Common Solution: Checklists, explicit sign-offs, verification steps

By learning from healthcare's decades of research, we can prevent software failures before they happen.

📖 Full Level 5 Documentation | Blog Post

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Philosophy

Systems Thinking Integration

The Empathy integrates Donella Meadows' leverage points:

1. Information flows: Provide the right data at the right time
2. Feedback loops: Create self-correcting systems
3. System structure: Design frameworks that naturally produce good outcomes
4. Paradigms: Shift from reactive to anticipatory thinking

First Principles from Naval Ravikant

• Clear thinking without emotional noise
• Leverage through systems, not just effort
• Compound effects from iterative improvement
• Specific knowledge > General advice

Tactical Empathy from Chris Voss

• Calibrated questions to uncover true needs
• Labeling emotions to build trust
• Mirroring to ensure understanding
• "No-oriented questions" to find blockers

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Documentation

• 📚 Framework Guide - Complete technical documentation
• 🎓 Non-Technical Guide - Accessible introduction
• 🧑‍🏫 Teaching AI - Alignment and collaboration patterns
• 💻 Coach Examples - Production-ready IDE integrations

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Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Ways to contribute:

• Implement new agents for different domains
• Add examples for other programming languages
• Improve documentation
• Report bugs and suggest features
• Share your implementations

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💖 Support This Project

The Empathy is source available (Fair Source 0.9) - free for students, educators, and small teams (≤5 employees). Commercial licensing required for larger organizations:

Commercial Support - $99/developer/year

• ✅ Priority bug fixes and feature requests
• ✅ Direct access to core development team (Slack/email)
• ✅ Security advisories and early notifications
• ✅ Guaranteed response times
• ✅ Upgrade assistance and migration help

Get Support →

Professional Services

• Custom Wizard Development - Domain-specific wizards for your industry
• Training & Workshops - Get your team productive in one day
• Enterprise Solutions - Hosted service, custom SLA, dedicated support

Learn More → | Contact Sales →

GitHub Sponsors

Support development directly: Sponsor on GitHub →

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License

This project is licensed under the Fair Source License, version 0.9 - see the LICENSE file for details.

License Terms

• ✅ Free for students and educators - Use for educational purposes at no cost
• ✅ Free for small businesses - Organizations with ≤5 employees use free forever
• ✅ Free for evaluation - 30-day trial for any organization size
• 💼 Commercial license required - $99/developer/year for organizations with 6+ employees
• 🔓 Auto-converts to open source - Becomes Apache 2.0 on January 1, 2029

Why Fair Source?

The Fair Source License balances:

• Free access for small teams - Students, educators, and small businesses (≤5 employees) use free
• Source code visibility - Full source available for security review, compliance, and learning
• Sustainable development - Commercial licensing funds ongoing development and support
• Future open source - Automatically converts to Apache 2.0 after 4 years

Commercial licensing: Email support@smartaimemory.com | Licensing FAQ →

---

Citation

If you use the Empathy in your research or product, please cite:

@software{empathy_framework_2025,
  author = {Roebuck, Patrick},
  title = {Empathy: A Five-Level Maturity Model for AI-Human Collaboration},
  year = {2025},
  publisher = {Deep Study AI, LLC},
  url = {https://github.com/Smart-AI-Memory/empathy-framework},
  license = {Fair-Source-0.9}
}

---

Support & Contact

Developer: Patrick Roebuck Email: patrick.roebuck1955@gmail.com Organization: Smart-AI-Memory GitHub: https://github.com/Smart-AI-Memory

Resources:

• Documentation: docs/
• Examples: examples/
• Issues: https://github.com/Smart-AI-Memory/empathy-framework/issues
• Discussions: https://github.com/Smart-AI-Memory/empathy-framework/discussions

---

Why Empathy vs Others?

The Empathy offers unique capabilities that set it apart from traditional code analysis tools:

Feature	Empathy	SonarQube	CodeClimate	GitHub Copilot
Level 4 Anticipatory Predictions	✅ Yes	❌ No	❌ No	❌ No
Philosophical Foundation	✅ Goleman, Voss, Naval	❌ Rules-based	❌ Rules-based	❌ Statistical
Source Available	✅ Fair Source 0.9	❌ No	❌ No	❌ No
Healthcare + Software	✅ Both domains	Software only	Software only	Software only
Free for Small Teams	✅ ≤5 employees	❌ Proprietary	❌ Proprietary	❌ Proprietary
Prevention vs Detection	✅ Anticipatory	Detection only	Detection only	Suggestion only
Price (Annual)	$99/dev (6+ employees)	$3,000+/year	$249/dev/year	$100/year

What Makes Level 4 Anticipatory Different?

Traditional tools tell you about problems now. Empathy predicts problems before they happen based on:

• Code trajectory analysis
• Team velocity patterns
• Dependency evolution
• Historical bug patterns
• Architecture stress points

Example: Instead of "This query is slow," you get "At your growth rate, this query will timeout when you hit 10,000 users. Here's the optimized version."

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IDE Extensions & Commercial Support

The Empathy uses Fair Source licensing. Commercial support and services available:

Priority Support - $99/developer/year

• Direct access to core development team
• Priority bug fixes and feature requests
• Security advisories and early notifications
• Guaranteed response times
• Upgrade assistance

Get Support →

Professional Services

• Custom Wizard Development - Domain-specific wizards for your industry
• Training & Workshops - Get your team productive quickly
• Enterprise Solutions - Hosted service, dedicated support, custom SLA

Learn More →

IDE Extensions (Coming Soon)

Free extensions for JetBrains & VS Code:

• One-click wizard access
• Inline code suggestions
• Real-time analysis
• Commit-time checks

In Development: JetBrains Marketplace & VS Code Marketplace extensions

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Acknowledgments

This framework synthesizes insights from:

• Daniel Goleman - Emotional Intelligence
• Chris Voss - Tactical Empathy
• Naval Ravikant - Clear Thinking and Leverage
• Donella Meadows - Systems Thinking
• Peter Senge - Learning Organizations

Special thanks to the AI Nurse Florence project for demonstrating Level 4 Anticipatory Empathy in healthcare.

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Built with ❤️ by Smart-AI-Memory

Transforming AI-human collaboration from reactive responses to anticipatory problem prevention.