ReasoningBank Intelligence

ReasoningBank 智能

此技能的作用

实现 ReasoningBank 的自适应学习系统，帮助 AI Agent 从经验中学习、识别模式并随时间优化策略。支持元认知能力和持续改进。

前置条件

• agentic-flow v1.5.11+
• AgentDB v1.0.4+（用于持久化）
• Node.js 18+

快速开始

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'
});

核心功能

1. 模式识别

// 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. 策略优化

// 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. 持续学习

// 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
});

高级用法

元学习

// 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']
  }
});

迁移学习

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

自适应 Agent

// 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;
  }
}

与 AgentDB 集成

// 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' }
});

性能指标

// 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}
`);

最佳实践

1. 坚持记录：记录所有任务结果，而不仅是成功案例
2. 提供上下文：丰富的上下文有助于改进模式匹配
3. 设置阈值：过滤低置信度的学习结果
4. 定期检查：审核已学习模式的质量
5. 使用向量搜索：启用语义模式匹配

故障排除

问题：建议质量差

解决方案：确保有足够的训练数据（每种任务类型 100+ 条经验）

问题：模式匹配缓慢

解决方案：在 AgentDB 中启用向量索引

问题：内存占用不断增大

解决方案：为旧经验设置 TTL，或启用清理

了解更多

• ReasoningBank 指南：agentic-flow$src$reasoningbank/README.md
• AgentDB 集成：packages$agentdb$docs$reasoningbank.md
• 模式学习：docs$reasoning$patterns.md