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.
npxskills add ruvnet/claude-flow--skill "AgentDB Memory Patterns"Loading…
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.
# 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 (integrates with Claude Code)
npx agentdb@latest mcp
# Add to Claude Code (one-time setup)
claude mcp add agentdb npx agentdb@latest mcp
# 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)
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
});
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
});
}
}
// 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'
});
// 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);
// Organize memory in hierarchy
await memory.organize({
immediate: recentMessages, // Last 10 messages
shortTerm: sessionContext, // Current session
longTerm: importantFacts, // Persistent facts
semantic: embeddedKnowledge // Vector search
});
// Periodically consolidate memories
await memory.consolidate({
strategy: 'importance', // Keep important memories
maxSize: 10000, // Size limit
minScore: 0.5 // Relevance threshold
});
# 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
# 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
# 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)
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 ${result.patternsMigrated} patterns`);
// Train learning model
const adapter = await createAgentDBAdapter({
enableLearning: true,
});
await adapter.train({
epochs: 50,
batchSize: 32,
});
// Get optimal strategy with reasoning
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'task-planning',
synthesizeContext: true,
optimizeMemory: true,
});
# List available plugins
npx agentdb@latest list-plugins
# List plugin templates
npx agentdb@latest list-templates
# Get plugin info
npx agentdb@latest plugin-info <name>
stats command to track performance# Check database size
npx agentdb@latest stats .$agents.db
# Enable quantization
# Use 'binary' (32x smaller) or 'scalar' (4x smaller)
# Enable HNSW indexing and caching
# Results: <100µs search time
# Automatic migration with validation
npx agentdb@latest migrate --source .swarm$memory.db
npx agentdb@latest mcp for Claude Code
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