reasoningbank-with-agentdb

name	ReasoningBank with AgentDB
version	1.0.0
description	Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
category	ai-ml
tags	["reasoningbank","agentdb","adaptive-learning","pattern-recognition","experience-replay"]
author	Smith Horn

ReasoningBank with AgentDB

What This Skill Does

Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.

Performance: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access.

Prerequisites

• Node.js 18+
• AgentDB v1.0.7+ (via agentic-flow)
• Understanding of reinforcement learning concepts (optional)

---

Quick Start with CLI

Initialize ReasoningBank Database

# Initialize AgentDB for ReasoningBank
npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536

# Start MCP server for Claude Code integration
npx agentdb@latest mcp
claude mcp add agentdb npx agentdb@latest mcp

Migrate from Legacy ReasoningBank

# Automatic migration with validation
npx agentdb@latest migrate --source .swarm/memory.db

# Verify migration
npx agentdb@latest stats ./.agentdb/reasoningbank.db

---

Quick Start with API

import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';

// Initialize ReasoningBank with AgentDB
const rb = await createAgentDBAdapter({
  dbPath: '.agentdb/reasoningbank.db',
  enableLearning: true,      // Enable learning plugins
  enableReasoning: true,      // Enable reasoning agents
  cacheSize: 1000,            // 1000 pattern cache
});

// Store successful experience
const query = "How to optimize database queries?";
const embedding = await computeEmbedding(query);

await rb.insertPattern({
  id: '',
  type: 'experience',
  domain: 'database-optimization',
  pattern_data: JSON.stringify({
    embedding,
    pattern: {
      query,
      approach: 'indexing + query optimization',
      outcome: 'success',
      metrics: { latency_reduction: 0.85 }
    }
  }),
  confidence: 0.95,
  usage_count: 1,
  success_count: 1,
  : .(),
  : .(),
});


 result =  rb.(embedding, {
  : ,
  : ,
  : ,              
  : ,    
});

.(, result.);
.(, result.);
.(, result.);

---

Core ReasoningBank Concepts

1. Trajectory Tracking

Track agent execution paths and outcomes:

// Record trajectory (sequence of actions)
const trajectory = {
  task: 'optimize-api-endpoint',
  steps: [
    { action: 'analyze-bottleneck', result: 'found N+1 query' },
    { action: 'add-eager-loading', result: 'reduced queries' },
    { action: 'add-caching', result: 'improved latency' }
  ],
  outcome: 'success',
  metrics: { latency_before: 2500, latency_after: 150 }
};

const embedding = await computeEmbedding(JSON.stringify(trajectory));

await rb.insertPattern({
  id: '',
  type: 'trajectory',
  domain: 'api-optimization',
  pattern_data: JSON.stringify({ embedding, pattern: trajectory }),
  confidence: 0.9,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.(),
});

2. Verdict Judgment

Judge whether a trajectory was successful:

// Retrieve similar past trajectories
const similar = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'api-optimization',
  k: 10,
});

// Judge based on similarity to successful patterns
const verdict = similar.memories.filter(m =>
  m.pattern.outcome === 'success' &&
  m.similarity > 0.8
).length > 5 ? 'likely_success' : 'needs_review';

console.log('Verdict:', verdict);
console.log('Confidence:', similar.memories[0]?.similarity || 0);

3. Memory Distillation

Consolidate similar experiences into patterns:

// Get all experiences in domain
const experiences = await rb.retrieveWithReasoning(embedding, {
  domain: 'api-optimization',
  k: 100,
  optimizeMemory: true,  // Automatic consolidation
});

// Distill into high-level pattern
const distilledPattern = {
  domain: 'api-optimization',
  pattern: 'For N+1 queries: add eager loading, then cache',
  success_rate: 0.92,
  sample_size: experiences.memories.length,
  confidence: 0.95
};

await rb.insertPattern({
  id: '',
  type: 'distilled-pattern',
  domain: 'api-optimization',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding(JSON.stringify(distilledPattern)),
    pattern: distilledPattern
  }),
  confidence: 0.95,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  : .(),
});

---

Integration with Reasoning Agents

AgentDB provides 4 reasoning modules that enhance ReasoningBank:

1. PatternMatcher

Find similar successful patterns:

const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'problem-solving',
  k: 10,
  useMMR: true,  // Maximal Marginal Relevance for diversity
});

// PatternMatcher returns diverse, relevant memories
result.memories.forEach(mem => {
  console.log(`Pattern: ${mem.pattern.approach}`);
  console.log(`Similarity: ${mem.similarity}`);
  console.log(`Success Rate: ${mem.success_count / mem.usage_count}`);
});

2. ContextSynthesizer

Generate rich context from multiple memories:

const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'code-optimization',
  synthesizeContext: true,  // Enable context synthesis
  k: 5,
});

// ContextSynthesizer creates coherent narrative
console.log('Synthesized Context:', result.context);
// "Based on 5 similar optimizations, the most effective approach
//  involves profiling, identifying bottlenecks, and applying targeted
//  improvements. Success rate: 87%"

3. MemoryOptimizer

Automatically consolidate and prune:

const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'testing',
  optimizeMemory: true,  // Enable automatic optimization
});

// MemoryOptimizer consolidates similar patterns and prunes low-quality
console.log('Optimizations:', result.optimizations);
// { consolidated: 15, pruned: 3, improved_quality: 0.12 }

4. ExperienceCurator

Filter by quality and relevance:

const result = await rb.retrieveWithReasoning(queryEmbedding, {
  domain: 'debugging',
  k: 20,
  minConfidence: 0.8,  // Only high-confidence experiences
});

// ExperienceCurator returns only quality experiences
result.memories.forEach(mem => {
  console.log(`Confidence: ${mem.confidence}`);
  console.log(`Success Rate: ${mem.success_count / mem.usage_count}`);
});

---

Legacy API Compatibility

AgentDB maintains 100% backward compatibility with legacy ReasoningBank:

import {
  retrieveMemories,
  judgeTrajectory,
  distillMemories
} from 'agentic-flow/reasoningbank';

// Legacy API works unchanged (uses AgentDB backend automatically)
const memories = await retrieveMemories(query, {
  domain: 'code-generation',
  agent: 'coder'
});

const verdict = await judgeTrajectory(trajectory, query);

const newMemories = await distillMemories(
  trajectory,
  verdict,
  query,
  { domain: 'code-generation' }
);

---

Performance Characteristics

• Pattern Search: 150x faster (100µs vs 15ms)
• Memory Retrieval: <1ms (with cache)
• Batch Insert: 500x faster (2ms vs 1s for 100 patterns)
• Trajectory Judgment: <5ms (including retrieval + analysis)
• Memory Distillation: <50ms (consolidate 100 patterns)

---

Advanced Patterns

Hierarchical Memory

Organize memories by abstraction level:

// Low-level: Specific implementation
await rb.insertPattern({
  type: 'concrete',
  domain: 'debugging/null-pointer',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { bug: 'NPE in UserService.getUser()', fix: 'Add null check' }
  }),
  confidence: 0.9,
  // ...
});

// Mid-level: Pattern across similar cases
await rb.insertPattern({
  type: 'pattern',
  domain: 'debugging',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { category: 'null-pointer', approach: 'defensive-checks' }
  }),
  confidence: 0.85,
  // ...
});

// High-level: General principle
await rb.insertPattern({
  type: 'principle',
  domain: 'software-engineering',
  pattern_data: JSON.stringify({
    embedding,
    pattern: { principle: 'fail-fast with clear errors' }
  }),
  confidence: ,
  
});

Multi-Domain Learning

Transfer learning across domains:

// Learn from backend optimization
const backendExperience = await rb.retrieveWithReasoning(embedding, {
  domain: 'backend-optimization',
  k: 10,
});

// Apply to frontend optimization
const transferredKnowledge = backendExperience.memories.map(mem => ({
  ...mem,
  domain: 'frontend-optimization',
  adapted: true,
}));

---

CLI Operations

Database Management

# Export trajectories and patterns
npx agentdb@latest export ./.agentdb/reasoningbank.db ./backup.json

# Import experiences
npx agentdb@latest import ./experiences.json

# Get statistics
npx agentdb@latest stats ./.agentdb/reasoningbank.db
# Shows: total patterns, domains, confidence distribution

Migration

# Migrate from legacy ReasoningBank
npx agentdb@latest migrate --source .swarm/memory.db --target .agentdb/reasoningbank.db

# Validate migration
npx agentdb@latest stats .agentdb/reasoningbank.db

---

Troubleshooting

Issue: Migration fails

# Check source database exists
ls -la .swarm/memory.db

# Run with verbose logging
DEBUG=agentdb:* npx agentdb@latest migrate --source .swarm/memory.db

Issue: Low confidence scores

// Enable context synthesis for better quality
const result = await rb.retrieveWithReasoning(embedding, {
  synthesizeContext: true,
  useMMR: true,
  k: 10,
});

Issue: Memory growing too large

// Enable automatic optimization
const result = await rb.retrieveWithReasoning(embedding, {
  optimizeMemory: true,  // Consolidates similar patterns
});

// Or manually optimize
await rb.optimize();

---

Learn More

• AgentDB Integration: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
• GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
• MCP Integration: npx agentdb@latest mcp
• Website: https://agentdb.ruv.io

---

Category: Machine Learning / Reinforcement Learning Difficulty: Intermediate Estimated Time: 20-30 minutes