feat: CRM Clinicas SaaS - MVP completo

- Auth: Login/Register con creacion de clinica - Dashboard: KPIs reales, graficas recharts - Pacientes: CRUD completo con busqueda - Agenda: FullCalendar, drag-and-drop, vista recepcion - Expediente: Notas SOAP, signos vitales, CIE-10 - Facturacion: Facturas con IVA, campos CFDI SAT - Inventario: Productos, stock, movimientos, alertas - Configuracion: Clinica, equipo, catalogo servicios - Supabase self-hosted: 18 tablas con RLS multi-tenant - Docker + Nginx para produccion Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 07:04:14 +00:00
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+---
+name: "AgentDB Memory Patterns"
+description: "Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants."
+---
+
+# AgentDB Memory Patterns
+
+## What This Skill Does
+
+Provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions.
+
+**Performance**: 150x-12,500x faster than traditional solutions with 100% backward compatibility.
+
+## Prerequisites
+
+- Node.js 18+
+- AgentDB v1.0.7+ (via agentic-flow or standalone)
+- Understanding of agent architectures
+
+## Quick Start with CLI
+
+### Initialize AgentDB
+
+```bash
+# Initialize vector database
+npx agentdb@latest init ./agents.db
+
+# Or with custom dimensions
+npx agentdb@latest init ./agents.db --dimension 768
+
+# Use preset configurations
+npx agentdb@latest init ./agents.db --preset large
+
+# In-memory database for testing
+npx agentdb@latest init ./memory.db --in-memory
+```
+
+### Start MCP Server for Claude Code
+
+```bash
+# Start MCP server (integrates with Claude Code)
+npx agentdb@latest mcp
+
+# Add to Claude Code (one-time setup)
+claude mcp add agentdb npx agentdb@latest mcp
+```
+
+### Create Learning Plugin
+
+```bash
+# Interactive plugin wizard
+npx agentdb@latest create-plugin
+
+# Use template directly
+npx agentdb@latest create-plugin -t decision-transformer -n my-agent
+
+# Available templates:
+# - decision-transformer (sequence modeling RL)
+# - q-learning (value-based learning)
+# - sarsa (on-policy TD learning)
+# - actor-critic (policy gradient)
+# - curiosity-driven (exploration-based)
+```
+
+## Quick Start with API
+
+```typescript
+import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
+
+// Initialize with default configuration
+const adapter = await createAgentDBAdapter({
+  dbPath: '.agentdb/reasoningbank.db',
+  enableLearning: true,      // Enable learning plugins
+  enableReasoning: true,      // Enable reasoning agents
+  quantizationType: 'scalar', // binary | scalar | product | none
+  cacheSize: 1000,            // In-memory cache
+});
+
+// Store interaction memory
+const patternId = await adapter.insertPattern({
+  id: '',
+  type: 'pattern',
+  domain: 'conversation',
+  pattern_data: JSON.stringify({
+    embedding: await computeEmbedding('What is the capital of France?'),
+    pattern: {
+      user: 'What is the capital of France?',
+      assistant: 'The capital of France is Paris.',
+      timestamp: Date.now()
+    }
+  }),
+  confidence: 0.95,
+  usage_count: 1,
+  success_count: 1,
+  created_at: Date.now(),
+  last_used: Date.now(),
+});
+
+// Retrieve context with reasoning
+const context = await adapter.retrieveWithReasoning(queryEmbedding, {
+  domain: 'conversation',
+  k: 10,
+  useMMR: true,              // Maximal Marginal Relevance
+  synthesizeContext: true,    // Generate rich context
+});
+```
+
+## Memory Patterns
+
+### 1. Session Memory
+```typescript
+class SessionMemory {
+  async storeMessage(role: string, content: string) {
+    return await db.storeMemory({
+      sessionId: this.sessionId,
+      role,
+      content,
+      timestamp: Date.now()
+    });
+  }
+
+  async getSessionHistory(limit = 20) {
+    return await db.query({
+      filters: { sessionId: this.sessionId },
+      orderBy: 'timestamp',
+      limit
+    });
+  }
+}
+```
+
+### 2. Long-Term Memory
+```typescript
+// Store important facts
+await db.storeFact({
+  category: 'user_preference',
+  key: 'language',
+  value: 'English',
+  confidence: 1.0,
+  source: 'explicit'
+});
+
+// Retrieve facts
+const prefs = await db.getFacts({
+  category: 'user_preference'
+});
+```
+
+### 3. Pattern Learning
+```typescript
+// Learn from successful interactions
+await db.storePattern({
+  trigger: 'user_asks_time',
+  response: 'provide_formatted_time',
+  success: true,
+  context: { timezone: 'UTC' }
+});
+
+// Apply learned patterns
+const pattern = await db.matchPattern(currentContext);
+```
+
+## Advanced Patterns
+
+### Hierarchical Memory
+```typescript
+// Organize memory in hierarchy
+await memory.organize({
+  immediate: recentMessages,    // Last 10 messages
+  shortTerm: sessionContext,    // Current session
+  longTerm: importantFacts,     // Persistent facts
+  semantic: embeddedKnowledge   // Vector search
+});
+```
+
+### Memory Consolidation
+```typescript
+// Periodically consolidate memories
+await memory.consolidate({
+  strategy: 'importance',       // Keep important memories
+  maxSize: 10000,              // Size limit
+  minScore: 0.5                // Relevance threshold
+});
+```
+
+## CLI Operations
+
+### Query Database
+
+```bash
+# Query with vector embedding
+npx agentdb@latest query ./agents.db "[0.1,0.2,0.3,...]"
+
+# Top-k results
+npx agentdb@latest query ./agents.db "[0.1,0.2,0.3]" -k 10
+
+# With similarity threshold
+npx agentdb@latest query ./agents.db "0.1 0.2 0.3" -t 0.75
+
+# JSON output
+npx agentdb@latest query ./agents.db "[...]" -f json
+```
+
+### Import/Export Data
+
+```bash
+# Export vectors to file
+npx agentdb@latest export ./agents.db ./backup.json
+
+# Import vectors from file
+npx agentdb@latest import ./backup.json
+
+# Get database statistics
+npx agentdb@latest stats ./agents.db
+```
+
+### Performance Benchmarks
+
+```bash
+# Run performance benchmarks
+npx agentdb@latest benchmark
+
+# Results show:
+# - Pattern Search: 150x faster (100µs vs 15ms)
+# - Batch Insert: 500x faster (2ms vs 1s)
+# - Large-scale Query: 12,500x faster (8ms vs 100s)
+```
+
+## Integration with ReasoningBank
+
+```typescript
+import { createAgentDBAdapter, migrateToAgentDB } from 'agentic-flow/reasoningbank';
+
+// Migrate from legacy ReasoningBank
+const result = await migrateToAgentDB(
+  '.swarm/memory.db',           // Source (legacy)
+  '.agentdb/reasoningbank.db'   // Destination (AgentDB)
+);
+
+console.log(`✅ Migrated ${result.patternsMigrated} patterns`);
+
+// Train learning model
+const adapter = await createAgentDBAdapter({
+  enableLearning: true,
+});
+
+await adapter.train({
+  epochs: 50,
+  batchSize: 32,
+});
+
+// Get optimal strategy with reasoning
+const result = await adapter.retrieveWithReasoning(queryEmbedding, {
+  domain: 'task-planning',
+  synthesizeContext: true,
+  optimizeMemory: true,
+});
+```
+
+## Learning Plugins
+
+### Available Algorithms (9 Total)
+
+1. **Decision Transformer** - Sequence modeling RL (recommended)
+2. **Q-Learning** - Value-based learning
+3. **SARSA** - On-policy TD learning
+4. **Actor-Critic** - Policy gradient with baseline
+5. **Active Learning** - Query selection
+6. **Adversarial Training** - Robustness
+7. **Curriculum Learning** - Progressive difficulty
+8. **Federated Learning** - Distributed learning
+9. **Multi-task Learning** - Transfer learning
+
+### List and Manage Plugins
+
+```bash
+# List available plugins
+npx agentdb@latest list-plugins
+
+# List plugin templates
+npx agentdb@latest list-templates
+
+# Get plugin info
+npx agentdb@latest plugin-info <name>
+```
+
+## Reasoning Agents (4 Modules)
+
+1. **PatternMatcher** - Find similar patterns with HNSW indexing
+2. **ContextSynthesizer** - Generate rich context from multiple sources
+3. **MemoryOptimizer** - Consolidate similar patterns, prune low-quality
+4. **ExperienceCurator** - Quality-based experience filtering
+
+## Best Practices
+
+1. **Enable quantization**: Use scalar/binary for 4-32x memory reduction
+2. **Use caching**: 1000 pattern cache for <1ms retrieval
+3. **Batch operations**: 500x faster than individual inserts
+4. **Train regularly**: Update learning models with new experiences
+5. **Enable reasoning**: Automatic context synthesis and optimization
+6. **Monitor metrics**: Use `stats` command to track performance
+
+## Troubleshooting
+
+### Issue: Memory growing too large
+```bash
+# Check database size
+npx agentdb@latest stats ./agents.db
+
+# Enable quantization
+# Use 'binary' (32x smaller) or 'scalar' (4x smaller)
+```
+
+### Issue: Slow search performance
+```bash
+# Enable HNSW indexing and caching
+# Results: <100µs search time
+```
+
+### Issue: Migration from legacy ReasoningBank
+```bash
+# Automatic migration with validation
+npx agentdb@latest migrate --source .swarm/memory.db
+```
+
+## Performance Characteristics
+
+- **Vector Search**: <100µs (HNSW indexing)
+- **Pattern Retrieval**: <1ms (with cache)
+- **Batch Insert**: 2ms for 100 patterns
+- **Memory Efficiency**: 4-32x reduction with quantization
+- **Backward Compatibility**: 100% compatible with ReasoningBank API
+
+## Learn More
+
+- GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
+- Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
+- MCP Integration: `npx agentdb@latest mcp` for Claude Code
+- Website: https://agentdb.ruv.io