bx-ai-memory

name	bx-ai-memory
description	Use this skill when implementing memory in BoxLang AI: aiMemory() types (windowed, summary, session, file, cache, JDBC, vector), multi-tenant isolation with userId and conversationId, using memory with agents and pipelines, and choosing the right memory type.

bx-ai: Memory Systems

aiMemory() BIF

// Signature
aiMemory( type, key="", userId="", conversationId="", config={} )

• type — memory type name (see table below)
• key — unique identifier for this memory instance
• userId — tenant user identifier (multi-tenant isolation)
• conversationId — isolate separate conversations for the same user
• config — type-specific configuration struct

Memory Types

Type	Best For	Persistence
windowed	Last N messages	In-memory
summary	Long conversations (auto-summarizes)	In-memory
session	Single request/session	In-memory
file	Simple persistence across restarts	File system
cache	Fast shared memory	CacheBox
jdbc	Multi-server production use	Database
chroma	Semantic search (vector)	ChromaDB
pinecone	Semantic search (vector)	Pinecone
weaviate	Semantic search (vector)	Weaviate
in-memory-vector	Dev/test semantic search	In-memory

Creating Memory

// Windowed: keeps last N messages
memory = aiMemory( "windowed", config: { maxMessages: 20 } )

// Summary: automatically compresses old messages into a summary
memory = aiMemory( "summary", config: {
    maxMessages    : 10,         // keep last 10 messages before summarizing
    summaryProvider: "openai"    // which provider does the summarization
})

// Session: lives for the duration of the current request
memory = aiMemory( "session" )

// File: persists conversations to disk
memory = aiMemory( "file", config: {
    filePath: expandPath( "./data/conversations" )
})

// JDBC: stored in a database table (production-ready)
memory = aiMemory( "jdbc", config: {
    datasource: "myApp",
    table     : "ai_conversations"
})

// Cache: uses CacheBox for shared, fast access
memory = aiMemory( "cache", config: {
    cacheName: "default"
})

Multi-Tenant Isolation

All memory types support isolation via userId and conversationId:

// Isolate per user
memory = aiMemory( "windowed",
    key   : createUUID(),
    userId: "user-alice",
    config: { maxMessages: 10 }
)

// Isolate per conversation (same user, different chats)
supportMemory = aiMemory( "windowed",
    key           : createUUID(),
    userId        : "user-alice",
    conversationId: "support-ticket-456",
    config        : { maxMessages: 20 }
)

salesMemory = aiMemory( "windowed",
    key           : createUUID(),
    userId        : "user-alice",
    conversationId: "sales-inquiry-789",
    config        : { maxMessages: 20 }
)

Using Memory with an Agent

// Create a persistent memory instance
memory = aiMemory( "windowed",
    key   : "chat-#session.sessionId#",
    userId: auth.getCurrentUserId(),
    config: { maxMessages: 30 }
)

agent = aiAgent(
    name        : "SupportBot",
    instructions: "You are a helpful support agent. Remember the user's context.",
    memory      : memory
)

// Each run() call uses and updates the memory
agent.run( "I'm having trouble with my subscription." )
agent.run( "It's been broken for 3 days." )
agent.run( "Can you summarize my issue?"  )
// → Agent remembers both earlier messages

Memory API

// Direct memory manipulation
memory.add( "user", "My name is Alice" )
memory.add( "assistant", "Hello Alice, how can I help?" )

// Get all messages
messages = memory.getMessages()

// Get recent N messages
recent = memory.getMessages( 5 )

// Clear memory
memory.clear()

// Get memory size
count = memory.size()

Vector Memory (Semantic Search)

For RAG and semantic retrieval, see the RAG skill. Quick reference:

// In-memory vector store (dev/testing)
vectorMem = aiMemory( "in-memory-vector", config: {
    embeddingProvider: "openai",
    embeddingModel   : "text-embedding-3-small"
})

// ChromaDB (production)
vectorMem = aiMemory( "chroma", config: {
    collection       : "knowledge_base",
    embeddingProvider: "openai",
    serverUrl        : "http://localhost:8000"
})

// Multi-tenant vector memory
aliceMem = aiMemory( "chroma",
    key   : createUUID(),
    userId: "alice",
    config: { collection: "user_notes", embeddingProvider: "openai" }
)

// Add documents
vectorMem.add( "BoxLang is a modern JVM language" )

// Retrieve semantically similar content
results = vectorMem.getRelevant( "What language runs on the JVM?", 5 )

Choosing the Right Memory Type

• Development / testing → windowed or in-memory-vector
• Single user, single server → file or cache
• Multi-server production → jdbc
• RAG / document search → chroma, pinecone, or weaviate
• Long conversations → summary to avoid context overflow
• Multi-tenant apps → any type with userId + conversationId

Common Pitfalls

• ❌ Do NOT store API keys or secrets in memory
• ❌ Do NOT use session memory for multi-turn conversations across requests
• ✅ Always set userId in multi-user applications to prevent data leakage
• ✅ Use summary memory for customer support bots with long conversations
• ✅ Reuse the same memory instance across multiple agent.run() calls for continuity