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reasoningbank-adaptive-learning-with-agentdb

// 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.

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stars:194
forks:37
updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
skill_idwhen-implementing-adaptive-learning-use-reasoningbank-agentdb
namereasoningbank-adaptive-learning-with-agentdb
descriptionImplement 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.
version1.0.0
categoryagentdb
subcategoryadaptive-learning
trigger_patternwhen-implementing-adaptive-learning
agentsml-developer,safla-neural,performance-analyzer
complexityadvanced
estimated_duration8-10 hours
prerequisitesAgentDB advanced features,Reinforcement learning concepts,Neural network understanding
outputsReasoningBank system,Trajectory tracking,Verdict judgment system,Memory distillation pipeline,Pattern recognition
validation_criteriaTrajectories tracked accurately,Verdicts judged correctly,Patterns learned and applied,Decision quality improves over time
evidence_based_techniquesTrajectory analysis,Verdict evaluation,Pattern mining,Self-improvement loops
metadata[object Object]

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