aiskillstore/reasoningbank-adaptive-learning-with-agentdb

ReasoningBank Adaptive Learning with AgentDB

Overview

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database for trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Build self-learning agents that improve decision-making through experience.

SOP Framework: 5-Phase Adaptive Learning

Phase 1: Initialize ReasoningBank (1-2 hours)

• Setup AgentDB with ReasoningBank
• Configure trajectory tracking
• Initialize verdict system

Phase 2: Track Trajectories (2-3 hours)

• Record agent decisions
• Store reasoning paths
• Capture context and outcomes

Phase 3: Judge Verdicts (2-3 hours)

• Evaluate decision quality
• Score reasoning paths
• Identify successful patterns

Phase 4: Distill Memory (2-3 hours)

• Extract learned patterns
• Consolidate successful strategies
• Prune ineffective approaches

Phase 5: Apply Learning (1-2 hours)

• Use learned patterns in decisions
• Improve future reasoning
• Measure improvement

Quick Start

import { AgentDB, ReasoningBank } from 'reasoningbank-agentdb';

// Initialize
const db = new AgentDB({
  name: 'reasoning-db',
  dimensions: 768,
  features: { reasoningBank: true }
});

const reasoningBank = new ReasoningBank({
  database: db,
  trajectoryWindow: 1000,
  verdictThreshold: 0.7
});

// Track trajectory
await reasoningBank.trackTrajectory({
  agent: 'agent-1',
  decision: 'action-A',
  reasoning: 'Because X and Y',
  context: { state: currentState },
  timestamp: Date.now()
});

// Judge verdict
const verdict = await reasoningBank.judgeVerdict({
  trajectory: trajectoryId,
  outcome: { success: true, reward: 10 },
  criteria: ['efficiency', 'correctness']
});

// Learn patterns
const patterns = await reasoningBank.distillPatterns({
  minSupport: 0.1,
  confidence: 0.8
});

// Apply learning
const decision = await reasoningBank.makeDecision({
  context: currentContext,
  useLearned: true
});

ReasoningBank Components

Trajectory Tracking

const trajectory = {
  agent: 'agent-1',
  steps: [
    { state: s0, action: a0, reasoning: r0 },
    { state: s1, action: a1, reasoning: r1 }
  ],
  outcome: { success: true, reward: 10 }
};

await reasoningBank.storeTrajectory(trajectory);

Verdict Judgment

const verdict = await reasoningBank.judge({
  trajectory: trajectory,
  criteria: {
    efficiency: 0.8,
    correctness: 0.9,
    novelty: 0.6
  }
});

Memory Distillation

const distilled = await reasoningBank.distill({
  trajectories: recentTrajectories,
  method: 'pattern-mining',
  compression: 0.1 // Keep top 10%
});

Pattern Application

const enhanced = await reasoningBank.enhance({
  query: newProblem,
  patterns: learnedPatterns,
  strategy: 'case-based'
});

Success Metrics

• Trajectory tracking accuracy > 95%
• Verdict judgment accuracy > 90%
• Pattern learning efficiency
• Decision quality improvement over time
• 150x faster than traditional approaches

Additional Resources

• Full docs: SKILL.md
• ReasoningBank Guide: https://reasoningbank.dev
• AgentDB Integration: https://agentdb.dev/docs/reasoningbank