---
skill_id: when-implementing-adaptive-learning-use-reasoningbank-agentdb
name: reasoningbank-adaptive-learning-with-agentdb
description: Implement ReasoningBank adaptive learning with AgentDB for trajectory tracking, verdict judgment, memory distillation, and pattern recognition to build self-learning agents that improve decision-making through experience.
version: 1.0.0
category: agentdb
subcategory: adaptive-learning
trigger_pattern: "when-implementing-adaptive-learning"
agents:
  - ml-developer
  - safla-neural
  - performance-analyzer
complexity: advanced
estimated_duration: 8-10 hours
prerequisites:
  - AgentDB advanced features
  - Reinforcement learning concepts
  - Neural network understanding
outputs:
  - ReasoningBank system
  - Trajectory tracking
  - Verdict judgment system
  - Memory distillation pipeline
  - Pattern recognition
validation_criteria:
  - Trajectories tracked accurately
  - Verdicts judged correctly
  - Patterns learned and applied
  - Decision quality improves over time
evidence_based_techniques:
  - Trajectory analysis
  - Verdict evaluation
  - Pattern mining
  - Self-improvement loops
metadata:
  author: claude-flow
  created: 2025-10-30
  tags:
    - agentdb
    - reasoningbank
    - adaptive-learning
    - meta-learning
    - pattern-recognition
---

# 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

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

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

### Pattern Application
```typescript
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