ReasoningBank Adaptive Learning with AgentDB Skill

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

Agent Skills: ReasoningBank Adaptive Learning with AgentDB

Install this agent skill to your local

Skill Files