reasoningbank-adaptive-learning-with-agentdb

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":"2025-10-30T00:00:00.000Z","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

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.({
  : ,
  : ,
  : ,
  : { : currentState },
  : .()
});


 verdict =  reasoningBank.({
  : trajectoryId,
  : { : , :  },
  : [, ]
});


 patterns =  reasoningBank.({
  : ,
  : 
});


 decision =  reasoningBank.({
  : currentContext,
  : 
});