# ReasoningBank with AgentDB

> Implementa el aprendizaje adaptativo de ReasoningBank con la base de datos vectorial de AgentDB, 150x más rápida. Incluye seguimiento de trayectorias, veredictos y destilación de memoria.

Fuente: https://skillsagentes.com/skills/ruvnet/ruflo/reasoningbank-with-agentdb
Markdown: https://skillsagentes.com/skills/ruvnet/ruflo/reasoningbank-with-agentdb.md

Repositorio: https://github.com/ruvnet/ruflo
Autor: ruvnet
Licencia: MIT
Actualizado: hace 6 meses
Coste de contexto: 73 tok instalada, 2.7k tok al activarse, 2.7k tok con todos los archivos del bundle
Bundle: 1 archivo, 11 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent claude-code
# Cursor
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent cursor
# Codex
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent codex
# Gemini CLI
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent gemini
# Windsurf
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent windsurf
# Cline
npx -y skills add ruvnet/ruflo --skill reasoningbank-agentdb --agent cline
```

## Qué hace

- Implementa ReasoningBank con AgentDB para aprendizaje adaptativo hasta 150x-12.500x más rápido.
- Rastrea trayectorias de ejecución de agentes y emite veredictos de éxito o fracaso.
- Destila experiencias similares en patrones de alto nivel organizados en memoria jerárquica.
- Mantiene compatibilidad total con la API legacy de ReasoningBank.

## Cuándo usarla

- Construyes agentes con aprendizaje autónomo (self-learning).
- Necesitas optimizar la toma de decisiones basada en experiencias pasadas.
- Vas a implementar un sistema de experience replay.

## Qué la activa

- "Inicializa la base de datos ReasoningBank con AgentDB"
- "Migra mi ReasoningBank legacy a AgentDB"
- "Recupera experiencias similares para optimizar consultas de base de datos"

## Antes de instalar

- Requiere Node.js 18+ y AgentDB v1.0.7+ (vía agentic-flow).
- Necesita en el PATH: npx

## Archivos

- SKILL.md — 11 KB

## SKILL.md

Reproducido tal cual desde ruvnet/ruflo bajo MIT. Esta sección es el documento original y está en inglés.

# 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

```bash
# 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

```bash
# 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

```typescript
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:

```typescript
// 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:

```typescript
// 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:

```typescript
// 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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
// 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:

```typescript
// 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

```bash
# 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

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

### Issue: Memory growing too large
```typescript
// 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

## Dónde encaja

- Categoría: [Bases de datos](https://skillsagentes.com/categorias/bases-de-datos.md) — Diseño de esquemas, migraciones y optimización de consultas.
- Creador: [ruvnet](https://skillsagentes.com/creators/ruvnet.md) — 275 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Harness Gepa](https://skillsagentes.com/skills/ruvnet/ruflo/harness-gepa.md): Inspecciona y audita genomas GEPA: carga y valida un genoma, renderiza el system prompt que compila, o clasifica los modos de fallo de una transcripción de ejecución.
- [Deepseek Reason](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-reason.md): Completion en modo razonamiento contra deepseek-reasoner (R1) de DeepSeek. Devuelve el chain-of-thought por separado de la respuesta final. Lee DEEPSEEK_API_KEY y degrada si falta o la API no responde.
- [Deepseek Chat](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-chat.md): Completion de un solo turno contra el modelo deepseek-chat de DeepSeek vía /v1/chat/completions. Lee DEEPSEEK_API_KEY y degrada con status:degraded si falta o la API no responde. Para tareas sin razonamiento.
- [Adr Index](https://skillsagentes.com/skills/ruvnet/ruflo/adr-index.md): Construye o reconstruye el índice de ADRs y su grafo de dependencias ejecutando scripts/import.mjs, en vez de cientos de llamadas MCP.
- [Agntcy Status](https://skillsagentes.com/skills/ruvnet/ruflo/agntcy-status.md): Muestra el estado de la integración AGNTCY/SLIM/CASA: si los paquetes están instalados, qué transporte está activo y si el enforcement de CASA está habilitado.

---

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