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 });

Memory Patterns

1. Session Memory

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

// 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

// 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

// 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

// Periodically consolidate memories await memory.consolidate({ strategy: 'importance', // Keep important memories maxSize: 10000, // Size limit minScore: 0.5 // Relevance threshold });

CLI Operations

Query Database

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

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

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

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)

• Decision Transformer - Sequence modeling RL (recommended)

• Q-Learning - Value-based learning

• SARSA - On-policy TD learning

• Actor-Critic - Policy gradient with baseline

• Active Learning - Query selection

• Adversarial Training - Robustness

• Curriculum Learning - Progressive difficulty

• Federated Learning - Distributed learning

• Multi-task Learning - Transfer learning

List and Manage Plugins

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)

• PatternMatcher - Find similar patterns with HNSW indexing

• ContextSynthesizer - Generate rich context from multiple sources

• MemoryOptimizer - Consolidate similar patterns, prune low-quality

• ExperienceCurator - Quality-based experience filtering

Best Practices

• Enable quantization: Use scalar/binary for 4-32x memory reduction

• Use caching: 1000 pattern cache for <1ms retrieval

• Batch operations: 500x faster than individual inserts

• Train regularly: Update learning models with new experiences

• Enable reasoning: Automatic context synthesis and optimization

• Monitor metrics: Use stats command to track performance

Troubleshooting

Issue: Memory growing too large

Check database size

npx agentdb@latest stats ./agents.db

Enable quantization

Use 'binary' (32x smaller) or 'scalar' (4x smaller)

Issue: Slow search performance

Enable HNSW indexing and caching

Results: <100µs search time

Issue: Migration from legacy ReasoningBank

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