ReasoningBank Models - Complete Index

📚 Quick Navigation

🎯 Models

📖 Documentation by Topic

Getting Started

1. Quick Start (30 seconds)
2. Choose Your Model
3. Install Model
4. First Query

Using Models

1. Installation Methods - 4 ways to install
2. Model Selection Guide - Which model for what?
3. Usage Examples - CLI, JavaScript, Python
4. Integration Patterns - With agentic-flow, Claude Code
5. Troubleshooting - Common issues

Training Models

1. Quick Start Training - 100 patterns in minutes
2. Training Architecture - How it works
3. Step-by-Step Guide - Complete walkthrough
4. Benchmarking & Validation - Quality assurance
5. Advanced Techniques - Parallel training, merging

Tools & Scripts

See _scripts/README.md for detailed documentation.

🎓 Tutorials

Tutorial 1: Install and Use a Model (5 minutes)

# 1. Choose model
cd /workspaces/claude-code-flow/docs/reasoningbank/models/safla

# 2. Copy to claude-flow
cp memory.db ~/.swarm/memory.db

# 3. Try it
npx claude-flow@alpha memory query "API optimization best practices"

Tutorial 2: Merge Multiple Models (10 minutes)

See HOW-TO-USE.md - Method 2: Merge Multiple Models

Tutorial 3: Create Custom Model (30 minutes)

See HOW-TO-TRAIN.md - Create a Simple Model

Tutorial 4: Integrate with Agentic-Flow (15 minutes)

See HOW-TO-USE.md - Pattern 1: Agentic-Flow Integration

📊 Model Details

SAFLA (Self-Aware Feedback Loop Algorithm)

• 📁 Location: safla/
• 📚 Documentation: safla/README.md
• 🎯 Patterns: 2,000
• 💾 Size: 10.35 MB
• 📈 Confidence: 83.8%
• ✅ Success Rate: 90.3%
• 🔧 Training Script: safla/train-safla.js

Pattern Categories:

• Self-learning patterns (400)
• Feedback loop optimization (400)
• Confidence adjustment (400)
• Success/failure distillation (400)
• Recursive improvement (400)

Google Research (Strategy-Level Memory)

• 📁 Location: google-research/
• 📚 Documentation: google-research/README.md
• 🎯 Patterns: 3,000
• 💾 Size: 8.92 MB
• 📈 Confidence: 88.0%
• 📄 Paper: arXiv:2509.25140
• 🔧 Training Script: google-research/train-google.js

Pattern Categories:

• Success strategies (600)
• Failure learnings (600)
• MaTTS parallel (600)
• MaTTS sequential (600)
• Closed-loop learning (600)

Code Reasoning (Programming Best Practices)

• 📁 Location: code-reasoning/
• 📚 Documentation: code-reasoning/README.md
• 🎯 Patterns: 2,500
• 💾 Size: 2.66 MB
• 📈 Confidence: 91.5%
• ✅ Success Rate: 91.2%
• 🔧 Training Script: code-reasoning/train-code.js

Pattern Categories:

• Design patterns & architecture (500)
• Algorithm optimization (500)
• Code quality & refactoring (500)
• Language-specific practices (500)
• Debugging & error handling (500)

Problem Solving (Cognitive Diversity)

• 📁 Location: problem-solving/
• 📚 Documentation: problem-solving/README.md
• 🎯 Patterns: 2,000
• 💾 Size: 5.85 MB
• 📈 Confidence: 83.7%
• ✅ Success Rate: 84.6%
• 🔧 Training Script: problem-solving/train-problem.js

Pattern Categories:

• Convergent thinking (400)
• Divergent thinking (400)
• Lateral thinking (400)
• Systems thinking (400)
• Critical thinking (400)

Domain Expert (Multi-Domain Expertise)

• 📁 Location: domain-expert/
• 📚 Documentation: domain-expert/README.md
• 🎯 Patterns: 1,500
• 💾 Size: 2.39 MB
• 📈 Confidence: 89.4%
• ✅ Success Rate: 88.5%
• 🔧 Training Script: domain-expert/train-domain.js

Domains:

• DevOps & Infrastructure (300)
• Data Engineering & ML (300)
• Security & Compliance (300)
• API Design & Integration (300)
• Performance & Scalability (300)

🎯 Use Case Matrix

🔧 Common Tasks

Task: Install a model

Guide: HOW-TO-USE.md - Quick Start Time: 30 seconds

Task: Query patterns

Guide: HOW-TO-USE.md - Usage Examples Example:

npx claude-flow@alpha memory query "JWT authentication" --reasoningbank

Task: Merge models

Guide: HOW-TO-USE.md - Merge Multiple Models Time: 5 minutes

Task: Train custom model

Guide: HOW-TO-TRAIN.md - Quick Start Time: 30-60 minutes

Task: Validate model quality

Command: node _scripts/validation-suite.cjs <model-dir> <model-name> Time: 1 minute

Task: Benchmark performance

Command: node _scripts/benchmark-all.cjs Time: 2-3 minutes

🚀 Advanced Topics

Parallel Training with Agents

See HOW-TO-TRAIN.md - Parallel Training

Memory Coordination

See HOW-TO-TRAIN.md - Memory Coordination

Incremental Training

See HOW-TO-TRAIN.md - Incremental Training

Custom Embeddings

See HOW-TO-TRAIN.md - Step 3: Create Embeddings

Pattern Linking

See HOW-TO-TRAIN.md - Step 4: Create Pattern Links

📈 Performance Standards

All models meet these production criteria:

🤝 Contributing

Want to contribute a model? We'd love to have it!

Requirements:

1. ✅ Minimum 1,000 unique patterns
2. ✅ 100% embedding coverage
3. ✅ >70% average confidence
4. ✅ <10 KB per pattern
5. ✅ Comprehensive README.md
6. ✅ Validation report
7. ✅ Training script included

Submission Process:

1. Create model using HOW-TO-TRAIN.md
2. Run validation: node _scripts/validation-suite.cjs your-model your-model-name
3. Create PR with model directory
4. Include README.md and validation report

📝 License

All models are MIT licensed and free for commercial/non-commercial use.

💡 Support

• Documentation Issues: Open issue on GitHub
• Model Questions: See model-specific README.md
• Training Help: HOW-TO-TRAIN.md
• Usage Help: HOW-TO-USE.md

Last Updated: 2025-10-15 Total Models: 5 Total Patterns: 11,000 Total Storage: 29.17 MB

Happy reasoning! 🧠✨