@memberjunction/ai-agents

Complete framework for building and executing AI agents in MemberJunction. Provides the BaseAgent execution engine, pluggable agent type system (Loop and Flow agents), hierarchical sub-agent orchestration, action execution, memory management with notes and examples, payload management, conversation context with message lifecycle management, and reranker integration.

Architecture

graph TD
    subgraph "@memberjunction/ai-agents"
        BA["BaseAgent<br/>Core Execution Engine"]
        style BA fill:#2d8659,stroke:#1a5c3a,color:#fff

        AR["AgentRunner<br/>Orchestration Entry Point"]
        style AR fill:#2d8659,stroke:#1a5c3a,color:#fff

        subgraph "Agent Types"
            BAT["BaseAgentType"]
            style BAT fill:#7c5295,stroke:#563a6b,color:#fff
            LAT["LoopAgentType"]
            style LAT fill:#7c5295,stroke:#563a6b,color:#fff
            FAT["FlowAgentType"]
            style FAT fill:#7c5295,stroke:#563a6b,color:#fff
        end

        subgraph "Support Systems"
            PM["PayloadManager<br/>Data Flow Between Steps"]
            style PM fill:#b8762f,stroke:#8a5722,color:#fff
            PCA["PayloadChangeAnalyzer"]
            style PCA fill:#b8762f,stroke:#8a5722,color:#fff
            PFM["PayloadFeedbackManager"]
            style PFM fill:#b8762f,stroke:#8a5722,color:#fff
            ACI["AgentContextInjector<br/>Notes, Examples, Data Sources"]
            style ACI fill:#b8762f,stroke:#8a5722,color:#fff
            ADP["AgentDataPreloader<br/>Batch Metadata Loading"]
            style ADP fill:#b8762f,stroke:#8a5722,color:#fff
            MMA["MemoryManagerAgent<br/>Note/Example Management"]
            style MMA fill:#b8762f,stroke:#8a5722,color:#fff
        end
    end

    BA --> BAT
    BA --> PM
    BA --> ACI
    BA --> ADP
    AR --> BA

    subgraph Dependencies
        AIP["@memberjunction/ai-prompts<br/>AIPromptRunner"]
        style AIP fill:#2d6a9f,stroke:#1a4971,color:#fff

        AIE["@memberjunction/aiengine<br/>AIEngine"]
        style AIE fill:#2d6a9f,stroke:#1a4971,color:#fff

        ACT["@memberjunction/actions<br/>ActionEngineServer"]
        style ACT fill:#2d6a9f,stroke:#1a4971,color:#fff

        RR["@memberjunction/ai-reranker<br/>RerankerService"]
        style RR fill:#2d6a9f,stroke:#1a4971,color:#fff
    end

    AIP --> BA
    AIE --> BA
    ACT --> BA
    RR --> BA

Installation

npm install @memberjunction/ai-agents

Key Components

BaseAgent

The core execution engine that all agents use. Handles:

• Hierarchical prompt execution (agent type's system prompt as parent, agent's prompts as children)
• Action execution through the MJ Actions framework
• Sub-agent orchestration with full context propagation
• Conversation context management with automatic message compaction
• Memory retrieval (notes and examples) with optional reranking
• Payload data management across execution steps
• ForEach and While loop operations
• Comprehensive execution tracking (AIAgentRun, AIAgentRunStep records)

AgentRunner

High-level entry point for agent execution. Provides:

• Agent resolution by ID or entity reference
• Permission checking before execution
• Data preloading for performance
• Simplified execution interface

import { AgentRunner } from '@memberjunction/ai-agents';

const runner = new AgentRunner();
const result = await runner.ExecuteAgent({
    agentId: 'agent-uuid',
    conversationMessages: [{ role: 'user', content: 'Analyze Q3 sales trends' }],
    contextUser: currentUser,
    onProgress: (step) => console.log(`${step.step}: ${step.message}`)
});

Agent Type System

Agents execute using a pluggable type system. The type determines how the agent decides its next action after each LLM call.

BaseAgentType

Abstract base that all agent types extend. Defines the DetermineNextStep() interface that produces a BaseAgentNextStep decision:

| Step | Description | |---|---| | Chat | Send a message back to the user | | Actions | Execute one or more actions | | SubAgents | Delegate to sub-agents | | MoreInfo | Ask the user for additional information | | Retry | Retry the current step (e.g., after validation failure) | | End | Complete execution | | ForEach | Iterate over a collection | | While | Loop while a condition is true |

LoopAgentType

Conversational agent that runs in a loop: prompt -> decide -> act -> repeat. Best for interactive, chat-based agents. The LLM decides the next step at each iteration by producing a structured JSON response.

FlowAgentType

Step-based agent that follows a predefined flow graph. Each step has explicit paths to the next step based on conditions. Best for deterministic workflows where the execution path is known in advance.

graph LR
    subgraph "Loop Agent"
        L1["Prompt LLM"] --> L2["Parse Response"]
        L2 --> L3{Decision}
        L3 -->|"Actions"| L4["Execute Actions"]
        L4 --> L1
        L3 -->|"Chat"| L5["Reply to User"]
        L5 --> L1
        L3 -->|"End"| L6["Complete"]
    end

    subgraph "Flow Agent"
        F1["Step 1"] -->|"Path A"| F2["Step 2a"]
        F1 -->|"Path B"| F3["Step 2b"]
        F2 --> F4["Step 3"]
        F3 --> F4
        F4 --> F5["End"]
    end

    style L1 fill:#2d6a9f,stroke:#1a4971,color:#fff
    style L2 fill:#7c5295,stroke:#563a6b,color:#fff
    style L3 fill:#b8762f,stroke:#8a5722,color:#fff
    style L4 fill:#2d8659,stroke:#1a5c3a,color:#fff
    style L5 fill:#2d8659,stroke:#1a5c3a,color:#fff
    style L6 fill:#2d8659,stroke:#1a5c3a,color:#fff

    style F1 fill:#2d6a9f,stroke:#1a4971,color:#fff
    style F2 fill:#7c5295,stroke:#563a6b,color:#fff
    style F3 fill:#7c5295,stroke:#563a6b,color:#fff
    style F4 fill:#b8762f,stroke:#8a5722,color:#fff
    style F5 fill:#2d8659,stroke:#1a5c3a,color:#fff

PayloadManager

Manages data flow through agent execution:

• Stores key-value data accessible across all steps and sub-agents
• Supports typed payload changes requested by the LLM
• Validates and applies changes through PayloadChangeAnalyzer
• Provides feedback to the LLM about successful/failed changes via PayloadFeedbackManager

const manager = new PayloadManager();
manager.Set('customerData', { name: 'Acme', revenue: 1000000 });
const data = manager.Get('customerData');

AgentContextInjector

Injects contextual information into agent prompts:

• Retrieves relevant notes via vector similarity search
• Retrieves relevant examples for few-shot learning
• Injects data source content
• Applies reranking when configured

AgentDataPreloader

Optimizes agent startup by batch-loading all required metadata in parallel:

• Agent entity with all relationships
• Actions and their parameters
• Sub-agent data
• Prompt configurations

MemoryManagerAgent

Handles persistent memory operations for agents:

• Creating and updating agent notes
• Managing agent examples
• Scoped memory for multi-tenant deployments (UserScope support)

Usage

Basic Agent Execution

import { AgentRunner } from '@memberjunction/ai-agents';
import { ExecuteAgentParams } from '@memberjunction/ai-core-plus';

const runner = new AgentRunner();
const result = await runner.ExecuteAgent({
    agentId: 'my-agent-id',
    conversationMessages: [
        { role: 'user', content: 'What are the top 5 customers by revenue?' }
    ],
    contextUser: currentUser
});

if (result.success) {
    console.log(result.outputMessages);
}

With Sub-Agent Orchestration

Sub-agents are automatically discovered from the agent's relationships and invoked when the LLM requests delegation:

const result = await runner.ExecuteAgent({
    agentId: 'orchestrator-agent-id',
    conversationMessages: messages,
    contextUser: currentUser,
    onProgress: (step) => {
        // Track execution across agent hierarchy
        console.log(`[${step.agentName}] ${step.message}`);
    }
});

With Runtime Action Changes

const result = await runner.ExecuteAgent({
    agentId: 'my-agent-id',
    conversationMessages: messages,
    contextUser: currentUser,
    actionChanges: [
        { scope: 'global', mode: 'add', actionIds: ['crm-search-id'] },
        { scope: 'all-subagents', mode: 'remove', actionIds: ['delete-record-id'] }
    ]
});

With User Scope (Multi-Tenant)

const result = await runner.ExecuteAgent({
    agentId: 'my-agent-id',
    conversationMessages: messages,
    contextUser: currentUser,
    userScope: {
        primaryEntityName: 'Organizations',
        primaryRecordId: orgId,
        secondary: { TeamID: teamId }
    }
});

With Message Lifecycle Management

const result = await runner.ExecuteAgent({
    agentId: 'my-agent-id',
    conversationMessages: messages,
    contextUser: currentUser,
    messageExpirationOverride: {
        expirationTurns: 3,
        expirationMode: 'Compact',
        compactMode: 'First N Chars',
        compactLength: 500
    },
    onMessageLifecycle: (event) => {
        console.log(`${event.type}: ${event.reason} (saved ${event.tokensSaved} tokens)`);
    }
});

Architecture Documentation

For multi-tenant memory scoping (notes/examples), see AGENT_MEMORY_SCOPING.md.

Re-exports

For backward compatibility, this package re-exports the following from @memberjunction/ai-reranker:

• RerankerService
• RerankerConfiguration
• parseRerankerConfiguration
• RerankServiceResult
• RerankObservabilityOptions
• LLMReranker

New code should import these directly from @memberjunction/ai-reranker.

Dependencies

• @memberjunction/ai-prompts -- AIPromptRunner for prompt execution
• @memberjunction/aiengine -- AIEngine for metadata and vector search
• @memberjunction/ai-core-plus -- Shared types (ExecuteAgentParams, ExecuteAgentResult)
• @memberjunction/ai-engine-base -- Base metadata cache and permissions
• @memberjunction/ai -- Core AI abstractions
• @memberjunction/ai-reranker -- Two-stage retrieval reranking
• @memberjunction/actions -- Server-side action execution
• @memberjunction/actions-base -- Action framework base types
• @memberjunction/core -- MJ framework core
• @memberjunction/core-entities -- Generated entity classes
• @memberjunction/global -- Class factory and utilities
• lodash -- Utility functions