ReasoningBank Intelligence

What This Skill Does

Implements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.

Prerequisites

• agentic-flow v1.5.11+

• AgentDB v1.0.4+ (for persistence)

• Node.js 18+

Quick Start

import { ReasoningBank } from 'agentic-flow$reasoningbank';

// Initialize ReasoningBank const rb = new ReasoningBank({ persist: true, learningRate: 0.1, adapter: 'agentdb' // Use AgentDB for storage });

// Record task outcome await rb.recordExperience({ task: 'code_review', approach: 'static_analysis_first', outcome: { success: true, metrics: { bugs_found: 5, time_taken: 120, false_positives: 1 } }, context: { language: 'typescript', complexity: 'medium' } });

// Get optimal strategy const strategy = await rb.recommendStrategy('code_review', { language: 'typescript', complexity: 'high' });

Core Features

1. Pattern Recognition

// Learn patterns from data await rb.learnPattern({ pattern: 'api_errors_increase_after_deploy', triggers: ['deployment', 'traffic_spike'], actions: ['rollback', 'scale_up'], confidence: 0.85 });

// Match patterns const matches = await rb.matchPatterns(currentSituation);

2. Strategy Optimization

// Compare strategies const comparison = await rb.compareStrategies('bug_fixing', [ 'tdd_approach', 'debug_first', 'reproduce_then_fix' ]);

// Get best strategy const best = comparison.strategies[0]; console.log(Best: ${best.name} (score: ${best.score}));

3. Continuous Learning

// Enable auto-learning from all tasks await rb.enableAutoLearning({ threshold: 0.7, // Only learn from high-confidence outcomes updateFrequency: 100 // Update models every 100 experiences });

Advanced Usage

Meta-Learning

// Learn about learning await rb.metaLearn({ observation: 'parallel_execution_faster_for_independent_tasks', confidence: 0.95, applicability: { task_types: ['batch_processing', 'data_transformation'], conditions: ['tasks_independent', 'io_bound'] } });

Transfer Learning

// Apply knowledge from one domain to another await rb.transferKnowledge({ from: 'code_review_javascript', to: 'code_review_typescript', similarity: 0.8 });

Adaptive Agents

// Create self-improving agent class AdaptiveAgent { async execute(task: Task) { // Get optimal strategy const strategy = await rb.recommendStrategy(task.type, task.context);

// Execute with strategy
const result = await this.executeWithStrategy(task, strategy);

// Learn from outcome
await rb.recordExperience({
  task: task.type,
  approach: strategy.name,
  outcome: result,
  context: task.context
});

return result;

} }

Integration with AgentDB

// Persist ReasoningBank data await rb.configure({ storage: { type: 'agentdb', options: { database: '.$reasoning-bank.db', enableVectorSearch: true } } });

// Query learned patterns const patterns = await rb.query({ category: 'optimization', minConfidence: 0.8, timeRange: { last: '30d' } });

Performance Metrics

// Track learning effectiveness const metrics = await rb.getMetrics(); console.log( Total Experiences: ${metrics.totalExperiences} Patterns Learned: ${metrics.patternsLearned} Strategy Success Rate: ${metrics.strategySuccessRate} Improvement Over Time: ${metrics.improvement});

Best Practices

• Record consistently: Log all task outcomes, not just successes

• Provide context: Rich context improves pattern matching

• Set thresholds: Filter low-confidence learnings

• Review periodically: Audit learned patterns for quality

• Use vector search: Enable semantic pattern matching

Troubleshooting

Issue: Poor recommendations

Solution: Ensure sufficient training data (100+ experiences per task type)

Issue: Slow pattern matching

Solution: Enable vector indexing in AgentDB

Issue: Memory growing large

Solution: Set TTL for old experiences or enable pruning

Learn More

• ReasoningBank Guide: agentic-flow$src$reasoningbank/README.md

• AgentDB Integration: packages$agentdb$docs$reasoningbank.md

• Pattern Learning: docs$reasoning$patterns.md