AgentDB Memory Patterns

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

# 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

# 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

# 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

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
});