ReasoningBank with AgentDB

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,
  created_at: Date.now(),
  last_used: Date.now(),
});

// Retrieve similar experiences with reasoning
const result = await rb.retrieveWithReasoning(embedding, {
  domain: 'database-optimization',
  k: 5,
  useMMR: true, // Diverse results
  synthesizeContext: true, // Rich context synthesis
});

console.log('Memories:', result.memories);
console.log('Context:', result.context);
console.log('Patterns:', result.patterns);

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.now(),
});

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(),
  last_used: 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: 0.95,
  // ...
});

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