Memory Management

Agent memory management — from simple window memory to vector-backed RAG, per-call identity routing, multi-tenant tracking, and suspend/resume.

Agents automatically maintain conversation history through their memory system. Memory is retrieved before each model call and stored after each response.

🔄 Memory Flow

Basic Memory (Window Memory)

// Agent with conversation memory
agent = aiAgent(
    name       : "ChatBot",
    description: "A conversational assistant",
    memory     : aiMemory( "window" )
)

// First interaction
agent.run( "My name is Luis" )

// Second interaction — agent remembers
agent.run( "What's my name?" )
// → "Your name is Luis"

// Access memory messages
messages = agent.getMemoryMessages()

// Clear memory when needed
agent.clearMemory()

Multiple Memory Systems

Agents can use multiple memory instances simultaneously — useful for combining conversation history with vector/semantic memory:

agent = aiAgent(
    name   : "SmartAgent",
    memory : [
        aiMemory( "window" ),          // Recent conversation history
        aiMemory( "pinecone", {        // Semantic long-term memory
            collection        : "projects",
            embeddingProvider : "openai"
        } )
    ]
)

// Agent stores to all memory systems and retrieves from all
agent.run( "Remember this: BoxLang is awesome" )

Per-Call Identity Routing (v3.0+)

In v3.0, you can route memory operations to specific users and conversations by passing userId and conversationId per call — one agent instance can serve multiple users without sharing state:

// Single shared agent instance
agent = aiAgent(
    name  : "SupportBot",
    memory: aiMemory( "cache" )
)

// Route to user Alice, conversation #1
agent.run(
    input  : "What is BoxLang?",
    options: { userId: "alice", conversationId: "conv-001" }
)

// Route to user Bob, separate conversation
agent.run(
    input  : "Help me with billing",
    options: { userId: "bob", conversationId: "conv-002" }
)

// Alice's next message — picks up exactly where she left off
agent.run(
    input  : "Tell me more",
    options: { userId: "alice", conversationId: "conv-001" }
)

The underlying memory operations (add, getAll, clear, trim, seed) all accept userId and conversationId as optional per-call overrides, so routing happens transparently.

Suspend & Resume (v3.0+)

Agents can be suspended mid-run (e.g., by HumanInTheLoopMiddleware) and resumed later. A checkpointer memory backend stores the agent's state:

agent = aiAgent(
    name        : "ApprovalAgent",
    checkpointer: aiMemory( "cache" ),  // Stores suspend state
    middleware  : [ new HumanInTheLoopMiddleware() ]
)

// Run returns a suspension result when human approval is needed
result = agent.run( "Deploy to production" )

if ( result.isSuspended() ) {
    // Store threadId for later resumption
    threadId = result.getThreadId()
    println( "Waiting for approval. Thread: #threadId#" )
}

// Later — resume with a human decision
finalResponse = agent.resume(
    decision   : "approved",
    threadId   : threadId,
    editedData : { reason: "Approved by admin" }
)

For streaming agents, use resumeStream() with the same parameters.

🏢 Multi-Tenant Usage Tracking

Track AI usage per tenant for billing and cost allocation:

// Run with tenant context
response = agent.run(
    input  : "How do I reset my password?",
    options: {
        tenantId     : "customer_acme",
        usageMetadata: {
            department : "support",
            ticketId   : "TICK-12345",
            userId     : "[email protected]"
        }
    }
)

Usage data (tokens, model, tenant) is fired as an onAITokenCount event on every call:

// Listen for token usage across all tenants
BoxRegisterInterceptor( "onAITokenCount", function( data ) {
    cost = calculateCost( data.totalTokens, data.model )
    billingService.record( data.tenantId, cost, data.usageMetadata )
} )

See Multi-Tenant Memory Guide for a comprehensive multi-tenancy setup.

Memory Types Quick Reference

Type	Best For
window	Simple session conversations (in-memory, lost on restart)
cache	Persistent across requests within cache TTL
file	Long-lived conversations stored to disk as JSON
session	Web applications tied to HTTP session
summary	Long conversations (older messages are AI-summarized)
jdbc	Full database persistence with SQL queries
hybrid	Recent window + semantic retrieval combined
box / chroma / pinecone / etc.	Vector-backed semantic memory

See Memory Systems for complete configuration options.

Related Pages

• Memory Systems — All memory types, configuration

• Multi-Tenant Memory Guide — Multi-tenant patterns

• Vector Memory Systems — Vector/semantic memory

• Middleware — HumanInTheLoopMiddleware for suspend/resume