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Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dnyoussef/reasoningbank-adaptive-learning-with-agentdb
Metadata
Additional technical details for this skill
- tags
-
agentdb reasoningbank adaptive-learning meta-learning pattern-recognition
- author
- claude-flow
- created
- 1761782400
SKILL.md
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
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