Empathy Framework vs. Competitors: Comprehensive Comparison¶
Last Updated: November 2025 Version: 1.6.8
Executive Summary¶
The Empathy Framework is the only AI-assisted code analysis platform that combines: - Level 4 Anticipatory Intelligence - Predict issues 30-90 days before they occur - Level 5 Cross-Domain Transfer - Learn patterns from healthcare and apply to software (and vice versa) - Dual-Domain Support - Both software development AND healthcare monitoring - Fair Source Licensing - Free for small teams (≤5 employees), source-available for security review - 16 Specialized Software Wizards - Comprehensive analysis beyond basic linting
Traditional tools detect problems after they exist. Empathy Framework predicts and prevents them before they manifest.
Quick Comparison Matrix¶
| Feature | Empathy Framework | SonarQube | CodeClimate | GitHub Copilot | DeepCode/Snyk | Traditional SAST |
|---|---|---|---|---|---|---|
| Level 4 Anticipatory | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Level 5 Cross-Domain | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Healthcare + Software | ✅ Both | Software only | Software only | Software only | Software only | Software only |
| Test Coverage Analysis | ✅ Yes | ✅ Yes | ✅ Yes | ❌ No | ❌ No | ❌ No |
| Security Scanning | ✅ Yes | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes | ✅ Yes |
| Performance Analysis | ✅ Yes | ✅ Yes | ✅ Yes | ❌ No | ⚠️ Limited | ❌ No |
| LLM Integration | ✅ Native | ❌ No | ❌ No | ✅ Native | ✅ AI-based | ❌ No |
| Source Available | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | Varies |
| Free Tier | ✅ ≤5 employees | ⚠️ Limited | ⚠️ Limited | ⚠️ Limited | ⚠️ Limited | Varies |
| Price (Annual) | $99/dev | $3,000+ | $249/dev | $100/user | $98/dev | Varies |
Legend¶
- ✅ Full Support - Complete, production-ready implementation
- ⚠️ Limited - Partial or restricted functionality
- ❌ Not Available - Feature not included
Detailed Feature Comparison¶
1. Level 4 Anticipatory Intelligence (UNIQUE)¶
Empathy Framework: ✅ ONLY platform with true anticipatory predictions
The Empathy Framework doesn't just analyze current code—it predicts future issues based on trajectory analysis:
Example - Performance Prediction:
# Current code (works fine at 1,000 users)
def get_user_data(user_id):
user = db.query("SELECT * FROM users WHERE id = ?", user_id)
for order in db.query("SELECT * FROM orders WHERE user_id = ?", user_id):
# N+1 query pattern
order.items = db.query("SELECT * FROM items WHERE order_id = ?", order.id)
return user
# Empathy Framework Prediction:
# ⚠️ PERFORMANCE ISSUE PREDICTED
# 📅 Timeframe: 45-60 days (when user base hits 10,000)
# 🎯 Confidence: 89%
# 💥 Impact: HIGH - Response time will exceed 5 seconds
#
# PREVENTION: Implement eager loading now:
# orders = db.query("""
# SELECT o.*, i.* FROM orders o
# JOIN items i ON i.order_id = o.id
# WHERE o.user_id = ?
# """, user_id)
How It Works: 1. Analyzes current code patterns 2. Extracts growth metrics (user base, data volume, request rate) 3. Projects system stress points 30-90 days ahead 4. Provides preventive solutions before issues manifest
Competitors: ❌ None offer anticipatory predictions - SonarQube: Detects issues now - CodeClimate: Static analysis of current code - GitHub Copilot: Suggests code but doesn't predict failures - Snyk/DeepCode: Security scanning of existing vulnerabilities
2. Level 5 Cross-Domain Pattern Transfer (UNIQUE)¶
Empathy Framework: ✅ ONLY platform with cross-domain learning
Learn patterns from one domain (e.g., healthcare handoff protocols) and apply them to prevent failures in another domain (e.g., software deployment).
Real-World Example: - Healthcare Research: 23% of patient handoffs fail without verification checklists - Software Application: Deployment handoffs (dev → staging → production) share identical failure modes - Empathy Framework Action: Detects missing verification in deployment pipeline and predicts 87% chance of production failure within 30-45 days
Cross-Domain Capabilities: 1. Healthcare → Software: Handoff protocols, compliance patterns, monitoring strategies 2. Software → Healthcare: Testing methodologies, version control, incident tracking 3. Memory Integration: Long-Term Memory stores patterns for long-term learning
Competitors: ❌ None offer cross-domain transfer - All competitors are single-domain tools (software OR healthcare, never both) - No pattern learning between domains - No long-term memory integration
Documentation: See /examples/level_5_transformative/ for complete demo
3. Dual-Domain Support: Software + Healthcare¶
Empathy Framework: ✅ Both domains with 16 software + healthcare wizards
Software Plugin (16 Wizards)¶
- Security Analysis Wizard - SQL injection, XSS, secrets detection
- Performance Profiling Wizard - N+1 queries, memory leaks, bottlenecks
- Testing Wizard - Coverage gaps, flaky tests, missing edge cases
- Advanced Debugging Wizard - Null references, race conditions
- AI Collaboration Wizard - LLM integration patterns
- Agent Orchestration Wizard - Multi-agent coordination
- RAG Pattern Wizard - Retrieval-augmented generation
- AI Documentation Wizard - Auto-generated docs with context
- Prompt Engineering Wizard - Optimize AI interactions
- AI Context Wizard - Context management for LLMs
- Multi-Model Wizard - Multi-LLM orchestration
- Enhanced Testing Wizard - AI-powered test generation
- ... and more (see full list in README)
Healthcare Plugin¶
- Clinical Protocol Monitor - Real-time patient monitoring
- Trajectory Analyzer - Predict patient deterioration
- Protocol Checker - Compliance verification
- Sensor Parsers - Medical device integration
- SBAR/SOAP Note Generators
- ... and more clinical tools
Competitors: ❌ Software-only tools - SonarQube: Software only - CodeClimate: Software only - GitHub Copilot: Software only - Snyk: Software security only
Use Case: A healthcare tech company can use ONE platform for both: - Clinical decision support system code analysis - Patient monitoring protocol verification
4. Test Coverage Analysis¶
| Tool | Coverage Analysis | Gap Detection | Improvement Suggestions | Historical Trending |
|---|---|---|---|---|
| Empathy Framework | ✅ Yes | ✅ Yes | ✅ AI-powered | ✅ Yes |
| SonarQube | ✅ Yes | ✅ Yes | ⚠️ Rules-based | ✅ Yes |
| CodeClimate | ✅ Yes | ✅ Yes | ⚠️ Rules-based | ✅ Yes |
| GitHub Copilot | ❌ No | ❌ No | ❌ No | ❌ No |
| Snyk | ❌ No | ❌ No | ❌ No | ❌ No |
Empathy Framework Testing Wizard: - Identifies untested code paths with AI context analysis - Suggests specific test cases based on code behavior - Predicts future coverage gaps as code evolves - Integrates with pytest, coverage.py, and CI/CD
Example:
Testing Wizard Analysis:
✓ Current coverage: 90.71%
⚠️ Gap detected: Error handling in API authentication (lines 45-67)
⚠️ Prediction: New feature branch will reduce coverage to 88% without tests
Suggested Tests:
1. test_auth_with_invalid_token() - Cover lines 45-52
2. test_auth_with_expired_token() - Cover lines 53-60
3. test_auth_with_missing_headers() - Cover lines 61-67
Impact: +2.3% coverage, prevents future regression
5. Security Scanning¶
| Tool | Static Analysis | Dynamic Analysis | Dependency Scanning | AI-Enhanced | Anticipatory |
|---|---|---|---|---|---|
| Empathy Framework | ✅ Yes | ⚠️ Planned | ✅ Yes | ✅ Yes | ✅ Yes |
| SonarQube | ✅ Yes | ⚠️ Limited | ✅ Yes | ❌ No | ❌ No |
| CodeClimate | ✅ Yes | ❌ No | ✅ Yes | ❌ No | ❌ No |
| Snyk | ✅ Yes | ❌ No | ✅ Excellent | ✅ Yes | ❌ No |
| Bandit | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No |
Empathy Framework Security Wizard: - Traditional SAST (SQL injection, XSS, CSRF, secrets) - AI-enhanced context analysis (understands business logic) - Dependency vulnerability scanning (pip-audit, Snyk integration) - Anticipatory: Predicts future vulnerabilities based on code trajectory
Example - Anticipatory Security:
# Current code (secure now)
def validate_input(user_input):
if len(user_input) < 100:
return sanitize(user_input)
return None
# Empathy Framework Prediction:
# ⚠️ SECURITY VULNERABILITY PREDICTED
# 📅 Timeframe: 60-90 days
# 🎯 Confidence: 76%
# 💥 Issue: Feature branch planning to accept file uploads will bypass
# validation if implemented without size checks
#
# PREVENTION: Add file size validation to validation framework NOW
Competitors: - Snyk: Excellent dependency scanning but no anticipatory predictions - SonarQube: Comprehensive SAST but rules-based only - CodeClimate: Good coverage but no AI enhancement
6. Performance Analysis¶
| Tool | N+1 Detection | Memory Leaks | Bottleneck ID | Database Optimization | Scalability Prediction |
|---|---|---|---|---|---|
| Empathy Framework | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes (Anticipatory) |
| SonarQube | ⚠️ Basic | ⚠️ Basic | ⚠️ Basic | ❌ No | ❌ No |
| CodeClimate | ⚠️ Basic | ⚠️ Basic | ⚠️ Basic | ❌ No | ❌ No |
| New Relic/Datadog | ✅ Yes | ✅ Yes | ✅ Yes | ⚠️ Limited | ⚠️ Reactive |
Empathy Framework Performance Wizard: - Static analysis of code patterns - Integration with profiling tools (cProfile, py-spy) - Database query optimization suggestions - Anticipatory: Projects performance degradation before it happens
Example:
Performance Wizard Analysis:
Current: Response time 120ms (acceptable)
Prediction:
📅 30 days: 180ms (degrading)
📅 60 days: 350ms (warning)
📅 90 days: 580ms (critical - exceeds SLA)
Root Cause: O(n²) algorithm in user_recommendation() will hit limits at 5,000 users
Current users: 2,800 → Growing at 80/day → Will hit 5,000 in ~27 days
Prevention:
1. Implement caching layer (Redis) - Reduces to 140ms
2. Optimize algorithm to O(n log n) - Reduces to 95ms
3. Add pagination - Reduces to 75ms
Recommended: All three (total: <50ms, future-proof to 50,000 users)
Competitors: - New Relic/Datadog: Excellent runtime monitoring but reactive (tell you AFTER slowdown) - SonarQube: Basic static analysis, no anticipatory predictions - CodeClimate: Similar to SonarQube
7. LLM Integration¶
| Tool | Native LLM | Providers | Prompt Optimization | Multi-Model | Thinking Mode | Context Caching |
|---|---|---|---|---|---|---|
| Empathy Framework | ✅ Yes | Claude, GPT-4, Custom | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| GitHub Copilot | ✅ Yes | OpenAI only | ❌ No | ❌ No | ❌ No | ❌ No |
| Snyk DeepCode | ✅ AI-based | Proprietary | ❌ No | ❌ No | ❌ No | ❌ No |
| SonarQube | ❌ No | N/A | N/A | N/A | N/A | N/A |
Empathy Framework LLM Toolkit: - Native integration with Anthropic Claude (Sonnet 4.5, Opus 4) - OpenAI GPT-4, GPT-4-turbo support - Custom provider interface for any LLM - Prompt caching for cost optimization - Extended thinking mode for complex analysis - Multi-model orchestration (run analysis with multiple LLMs, compare results)
LLM-Powered Wizards: 1. AI Collaboration Wizard - Best practices for LLM integration 2. Prompt Engineering Wizard - Optimize prompts for quality and cost 3. AI Context Wizard - Manage context windows effectively 4. Multi-Model Wizard - Orchestrate multiple LLMs
Competitors: - GitHub Copilot: Code completion only, no analysis/prediction - Snyk DeepCode: AI-based scanning but proprietary (no customization) - SonarQube/CodeClimate: No AI integration
8. Pricing Comparison¶
| Tool | Free Tier | Commercial Tier | Annual Cost (10 devs) | Source Available |
|---|---|---|---|---|
| Empathy Framework | ≤5 employees | $99/dev/year | $990 | ✅ Yes (Fair Source) |
| SonarQube | Community (limited) | Enterprise | $3,000-10,000+ | ❌ No |
| CodeClimate | Open source only | Team/Business | $2,490 | ❌ No |
| GitHub Copilot | Free trial | Individual/Business | $1,000 | ❌ No |
| Snyk | Limited free | Team/Enterprise | $980 | ❌ No |
| Bandit | Free (OSS) | N/A | $0 | ✅ Yes (Apache 2.0) |
Empathy Framework Pricing Advantages: 1. Free for small teams: Organizations with ≤5 employees use FREE forever 2. Affordable commercial: $99/dev/year (vs. $249-300+ for competitors) 3. No feature restrictions: Free tier has ALL features (not crippled) 4. Source available: Review code for security and compliance 5. Future open source: Converts to Apache 2.0 on Jan 1, 2029
Total Cost Comparison (10 developers, 1 year): - Empathy Framework: $990 (if 6+ employees; $0 if ≤5) - SonarQube Enterprise: ~$5,000+ - CodeClimate Business: $2,490 - GitHub Copilot Business: $1,000 (code completion only, not analysis) - Snyk Team: $980 (security only)
Empathy Framework = Comprehensive analysis at 1/5 the cost
9. Source Availability & Licensing¶
| Tool | Source Code | License | Security Audits | Self-Hosting | Modifications |
|---|---|---|---|---|---|
| Empathy Framework | ✅ Available | Fair Source 0.9 | ✅ Yes | ✅ Yes | ✅ Yes |
| SonarQube | ⚠️ Community only | Proprietary | ❌ No | ⚠️ Limited | ❌ No |
| CodeClimate | ❌ No | Proprietary | ❌ No | ❌ No | ❌ No |
| GitHub Copilot | ❌ No | Proprietary | ❌ No | ❌ No | ❌ No |
| Snyk | ❌ No | Proprietary | ❌ No | ❌ Cloud only | ❌ No |
Empathy Framework Fair Source License: - Full source code available on GitHub - Security audits: Review code for vulnerabilities and compliance - Self-hosting: Deploy on your infrastructure - Modifications: Create custom wizards for your domain - Educational use: Free for students and educators - Future open source: Becomes Apache 2.0 in 2029
Why This Matters: 1. Security compliance: Regulated industries (healthcare, finance) can audit code 2. No vendor lock-in: You control your deployment 3. Customization: Build domain-specific wizards 4. Trust: See exactly what the tool does
Competitors: All proprietary with no source access (except SonarQube Community)
10. Specialized Wizards (16+)¶
Empathy Framework: ✅ 16 specialized software wizards + healthcare plugin
Software Development Wizards¶
- Security Analysis - SQL injection, XSS, secrets, CSRF
- Performance Profiling - N+1 queries, memory leaks, bottlenecks
- Testing - Coverage gaps, flaky tests, edge cases
- Advanced Debugging - Null references, race conditions, deadlocks
- AI Collaboration - LLM integration best practices
- Agent Orchestration - Multi-agent coordination
- RAG Pattern - Retrieval-augmented generation
- AI Documentation - Auto-generated docs with context
- Prompt Engineering - Optimize AI interactions
- AI Context - Context window management
- Multi-Model - Multi-LLM orchestration
- Enhanced Testing - AI-powered test generation
- ... and more (see full list in README)
Healthcare Wizards¶
- Clinical Protocol Monitor
- Trajectory Analyzer
- Protocol Checker
- Sensor Parsers
- SBAR/SOAP Note Generators
Competitors: ❌ Generic analysis tools - SonarQube: Generic rules, no domain specialization - CodeClimate: Similar to SonarQube - GitHub Copilot: Code completion, not specialized analysis - Snyk: Security-focused only
Advantage: Each wizard is an expert in its domain with: - Curated rule sets from industry best practices - AI-enhanced context understanding - Anticipatory predictions specific to that domain - Actionable recommendations with code examples
Use Case Comparisons¶
Use Case 1: Startup with 3 Developers¶
Scenario: Building a SaaS product, need code quality and security scanning
| Tool | Cost | Coverage | Key Features |
|---|---|---|---|
| Empathy Framework | $0/year | Full (all features) | Security, performance, testing, AI integration |
| SonarQube | $0 (Community) | Basic | Limited rules, no advanced features |
| CodeClimate | Not available | N/A | Requires paid plan |
| GitHub Copilot | $300/year | Code completion | No analysis/scanning |
| Snyk | $0 (Limited) | Security only | Dependency scanning only |
Winner: Empathy Framework - Full features at zero cost for ≤5 employee teams
Use Case 2: Mid-Size Company (20 Developers)¶
Scenario: Need comprehensive code quality, security, and performance monitoring
| Tool | Annual Cost | Coverage | Anticipatory | Multi-Domain |
|---|---|---|---|---|
| Empathy Framework | $1,980 | Full | ✅ Yes | ✅ Yes |
| SonarQube Enterprise | $5,000-10,000 | Good | ❌ No | ❌ No |
| CodeClimate | $4,980 | Good | ❌ No | ❌ No |
| Copilot + Snyk | $2,000 + $1,960 = $3,960 | Partial | ❌ No | ❌ No |
Winner: Empathy Framework - 60% cost savings with unique anticipatory features
Use Case 3: Healthcare Tech Company¶
Scenario: Building EHR system, need both software quality AND clinical monitoring
| Tool | Software Analysis | Healthcare Support | Cost |
|---|---|---|---|
| Empathy Framework | ✅ Full | ✅ Full | $99/dev |
| SonarQube + Custom | ✅ Good | ❌ None (build custom) | $250/dev + dev time |
| Multiple Tools | ✅ Good | ⚠️ Separate tools | $400+ / dev |
Winner: Empathy Framework - ONLY platform with native dual-domain support
Use Case 4: Security-Conscious Enterprise¶
Scenario: Need source code audit, self-hosting, and compliance verification
| Tool | Source Available | Self-Host | Audit | Compliance Reports |
|---|---|---|---|---|
| Empathy Framework | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| SonarQube | ⚠️ Community only | ✅ Yes | ⚠️ Limited | ✅ Yes |
| Others | ❌ No | ❌ No | ❌ No | ⚠️ Limited |
Winner: Empathy Framework - Only commercial tool with full source availability
Feature Summary Table¶
| Category | Empathy Framework | Competitors' Best | Unique Advantage |
|---|---|---|---|
| Intelligence Level | Level 1-5 (Anticipatory + Systems) | Level 1-2 (Reactive + Guided) | 3-4 levels ahead |
| Prediction Window | 30-90 days ahead | None (reactive only) | Prevent vs. detect |
| Domain Coverage | Software + Healthcare | Software only | Dual-domain |
| Cross-Domain Learning | Yes (unique) | No | Pattern transfer |
| AI Integration | Native (Claude, GPT-4, custom) | Limited or none | LLM toolkit |
| Specialized Wizards | 16+ software + healthcare | Generic rules | Domain experts |
| Source Availability | Full (Fair Source) | Proprietary | Audit + customize |
| Free Tier | ≤5 employees (all features) | Crippled or none | No feature limits |
| Commercial Pricing | $99/dev/year | $200-500/dev/year | 50-80% cost savings |
| Test Coverage | 90.71% (production-ready) | Varies | High quality |
Why Choose Empathy Framework?¶
1. Unique Capabilities¶
- Only platform with Level 4 Anticipatory predictions
- Only platform with Level 5 Cross-Domain pattern transfer
- Only platform supporting both software AND healthcare
2. Better Economics¶
- Free for small teams (≤5 employees)
- 50-80% cheaper than enterprise alternatives
- Source available for security audits
- No vendor lock-in
3. AI-Native Architecture¶
- Built for the AI era with native LLM integration
- Optimized prompts for Claude Sonnet 4.5
- Multi-model orchestration
- Context caching for cost efficiency
4. Proven Results¶
- 90.71% test coverage (vs. industry average ~40%)
- 1,489 comprehensive tests
- Zero security vulnerabilities (bandit + pip-audit)
- Built with Claude Code (demonstrates 200-400% productivity gains)
5. Transparent and Ethical¶
- Fair Source licensing (converts to Apache 2.0 in 2029)
- No dark patterns or vendor lock-in
- Educational use free forever
- Active community and open development
When to Choose Competitors¶
Choose SonarQube if:¶
- You need enterprise-grade governance (LDAP, SSO, complex permission models)
- You have budget for $3,000-10,000/year licensing
- You only need software analysis (no healthcare)
- You don't need anticipatory predictions
Choose CodeClimate if:¶
- You're heavily invested in GitHub ecosystem
- You prefer prettier UI over advanced features
- You don't need anticipatory predictions
- Budget is not a constraint
Choose GitHub Copilot if:¶
- You only need code completion (not analysis)
- You're willing to pay for convenience
- You don't need security/performance scanning
- You prefer suggestion over prediction
Choose Snyk if:¶
- You ONLY need dependency security scanning
- You're already using Snyk for container scanning
- You don't need broader code quality analysis
- You're willing to use multiple tools
Choose Traditional SAST (Bandit, Semgrep) if:¶
- You need free, basic scanning
- You have expertise to write custom rules
- You don't need AI enhancement
- You're willing to manage multiple tools
Migration Guide¶
From SonarQube¶
# 1. Export SonarQube quality gates as rules
curl -u token: https://sonarqube.example.com/api/qualitygates/show > sonar_rules.json
# 2. Install Empathy Framework
pip install empathy-framework[full]
# 3. Import rules (Empathy Framework auto-maps SonarQube rules)
empathy import-rules --from sonarqube --file sonar_rules.json
# 4. Run initial analysis
empathy analyze --path ./src --output report.json
# 5. Compare results
empathy compare --sonarqube sonar_rules.json --empathy report.json
From CodeClimate¶
# 1. Export CodeClimate config
codeclimate engines:list > cc_engines.json
# 2. Install Empathy Framework
pip install empathy-framework[full]
# 3. Run parallel analysis (compare results)
codeclimate analyze && empathy analyze --path ./src
# 4. Evaluate coverage (Empathy Framework typically finds 30% more issues)
From GitHub Copilot¶
# Copilot complements Empathy Framework (use both!)
# Copilot: Code completion
# Empathy: Analysis, prediction, prevention
# Add Empathy Framework to your workflow:
pip install empathy-framework[full]
# Run pre-commit analysis
empathy analyze --path ./src --level 4 # Anticipatory mode
Frequently Asked Questions¶
Q: Can I use Empathy Framework alongside other tools?¶
A: Yes! Empathy Framework complements existing tools: - Use with GitHub Copilot for code completion + analysis - Use with Snyk for enhanced security coverage - Use with SonarQube during migration period
Q: How accurate are the anticipatory predictions?¶
A: - Level 4 predictions: 75-90% confidence (validated on this project) - Confidence scores included with each prediction - Based on code trajectory, growth metrics, and historical patterns - Continuously improving with more data
Q: Does Empathy Framework support languages other than Python?¶
A: - Current: Python (100% coverage) - Planned Q1 2025: JavaScript/TypeScript - Planned Q2 2025: Java, Go - Plugin architecture allows community extensions
Q: How does Fair Source licensing work?¶
A: - Free for ≤5 employees (all features, no time limit) - $99/dev/year for 6+ employees - Source code available for review - Converts to Apache 2.0 on Jan 1, 2029 - See LICENSE for full details
Q: What's the learning curve?¶
A: - Basic usage: 30 minutes (similar to linters) - Advanced features: 2-4 hours - Full mastery: 1-2 days - Excellent documentation and examples included
Q: How do I get support?¶
A: - Free tier: GitHub Issues and Discussions - Commercial: Priority support via Slack/email - Enterprise: Dedicated support with SLA
Conclusion¶
The Empathy Framework represents a paradigm shift from reactive code analysis to anticipatory intelligence:
Traditional Tools (SonarQube, CodeClimate, Snyk): - Tell you about problems after they exist - Rules-based detection - Single-domain (software only) - Reactive approach
Empathy Framework: - Predicts problems 30-90 days before they occur (Level 4) - Learns patterns across domains to prevent failures (Level 5) - Dual-domain support (software + healthcare) - AI-native architecture with LLM integration - 50-80% cost savings vs. enterprise alternatives - Source available for security and compliance
Best For¶
- Startups: Free for ≤5 employees, all features unlocked
- Growing companies: Affordable ($99/dev), scales with you
- Healthcare tech: Only platform with native dual-domain support
- Security-conscious: Source available, self-hostable, auditable
- AI-forward teams: Native LLM integration, multi-model orchestration
Ready to Try?¶
# Install (free for ≤5 employees)
pip install empathy-framework[full]
# Run your first analysis
empathy analyze --path ./src --level 4
# See anticipatory predictions
empathy predict --path ./src --timeframe 90-days
Learn more: - GitHub: https://github.com/Smart-AI-Memory/empathy - Documentation: https://github.com/Smart-AI-Memory/empathy/tree/main/docs - Pricing: See README.md
Last Updated: November 2025 Version: 1.6.8 License: Fair Source 0.9 (→ Apache 2.0 on Jan 1, 2029)