When Implementing Adaptive Learning Use Reasoningbank Agentdb

/============================================================================/ /* REASONINGBANK ADAPTIVE LEARNING WITH AGENTDB SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: ReasoningBank Adaptive Learning with AgentDB version: 1.0.0 description: | [assert|neutral] ReasoningBank Adaptive Learning with AgentDB skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags:

• general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute ReasoningBank Adaptive Learning with AgentDB workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "ReasoningBank Adaptive Learning with AgentDB", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["ReasoningBank Adaptive Learning with AgentDB", "agentdb", "workflow"], context: "user needs ReasoningBank Adaptive Learning with AgentDB capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

ReasoningBank Adaptive Learning with AgentDB

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

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

• [assert|neutral] Trajectory tracking accuracy > 95% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Verdict judgment accuracy > 90% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Pattern learning efficiency [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Decision quality improvement over time [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] 150x faster than traditional approaches [ground:acceptance-criteria] [conf:0.90] [state:provisional]

MCP Requirements

This skill operates using AgentDB's npm package and API only. No additional MCP servers required.

All AgentDB/ReasoningBank operations are performed through:

• npm CLI: npx agentdb@latest
• TypeScript/JavaScript API: import { AgentDB, ReasoningBank } from 'reasoningbank-agentdb'

Additional Resources

• Full docs: SKILL.md
• ReasoningBank Guide: https://reasoningbank.dev
• AgentDB Integration: https://agentdb.dev/docs/reasoningbank

Core Principl

/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/ReasoningBank Adaptive Learning with AgentDB/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "ReasoningBank Adaptive Learning with AgentDB-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/

[commit|confident] REASONINGBANK ADAPTIVE LEARNING WITH AGENTDB_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]