/reasoningbank-agentdb

name: "ReasoningBank with AgentDB"
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."

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:', resul