Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
npxskills add ruvnet/claude-flow--skill "ReasoningBank Intelligence"Loading…
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.
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'
});
// 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);
// 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: ${best.score})`);
// Enable auto-learning from all tasks
await rb.enableAutoLearning({
threshold: 0.7, // Only learn from high-confidence outcomes
updateFrequency: 100 // Update models every 100 experiences
});
// Learn about learning
await rb.metaLearn({
observation: 'parallel_execution_faster_for_independent_tasks',
confidence: 0.95,
applicability: {
task_types: ['batch_processing', 'data_transformation'],
conditions: ['tasks_independent', 'io_bound']
}
});
// Apply knowledge from one domain to another
await rb.transferKnowledge({
from: 'code_review_javascript',
to: 'code_review_typescript',
similarity: 0.8
});
// Create self-improving agent
class AdaptiveAgent {
async execute(task: Task) {
// Get optimal strategy
const strategy = await rb.recommendStrategy(task.type, task.context);
// Execute with strategy
const result = await this.executeWithStrategy(task, strategy);
// Learn from outcome
await rb.recordExperience({
task: task.type,
approach: strategy.name,
outcome: result,
context: task.context
});
return result;
}
}
// Persist ReasoningBank data
await rb.configure({
storage: {
type: 'agentdb',
options: {
database: '.$reasoning-bank.db',
enableVectorSearch: true
}
}
});
// Query learned patterns
const patterns = await rb.query({
category: 'optimization',
minConfidence: 0.8,
timeRange: { last: '30d' }
});
// 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}
`);
Solution: Ensure sufficient training data (100+ experiences per task type)
Solution: Enable vector indexing in AgentDB
Solution: Set TTL for old experiences or enable pruning
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