MemberJunction AI Framework

A unified, provider-agnostic AI abstraction layer for TypeScript. Write your AI logic once; swap providers, models, and capabilities without changing application code.

The framework spans 47 packages covering LLMs, embeddings, image generation, audio synthesis, video generation, reranking, vector operations, agent orchestration, and protocol servers — with integrations for 25+ AI services from OpenAI and Anthropic to local inference engines like Ollama and LM Studio.

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Two Ways to Use It

Standalone (No MemberJunction Required)

Install @memberjunction/ai (the Core package) plus any provider. Zero database dependencies. Works in any TypeScript or JavaScript project.

import { ChatParams, ChatMessageRole } from "@memberjunction/ai";
import { AnthropicLLM } from "@memberjunction/ai-anthropic";

const llm = new AnthropicLLM(process.env.ANTHROPIC_API_KEY!);
const result = await llm.ChatCompletion({
    model: "claude-sonnet-4-20250514",
    messages: [{ role: ChatMessageRole.user, content: "Explain quantum tunneling." }],
});
console.log(result.data.choices[0].message.content);

That is the entire setup. No configuration files, no database, no metadata — just types and a provider.

MJ-Integrated (Full Platform)

When running inside MemberJunction, the AI framework gains metadata-driven model selection, prompt template management with hierarchical composition, cost tracking, agent orchestration, and vector synchronization — all configured through the MJ database and entity system.

import { AIPromptRunner } from "@memberjunction/ai-prompts";
import { AIPromptParams } from "@memberjunction/ai-core-plus";

// Model, provider, and prompt template resolved from MJ metadata
const runner = new AIPromptRunner();
const params = new AIPromptParams();
params.promptName = "Summarize Document";
params.data = { content: documentText };

const result = await runner.ExecutePrompt(params);

---

Architecture

graph TB
    subgraph Application["Application Layer"]
        APP["Your Code"]
    end

    subgraph Core["@memberjunction/ai  (Zero MJ Dependencies)"]
        BM["BaseLLM"]
        BE["BaseEmbeddings"]
        BIG["BaseImageGenerator"]
        BA["BaseAudio"]
        BV["BaseVideo"]
        BR["BaseReranker"]
        TYPES["Chat/Embed/Image Types"]
        ERR["ErrorAnalyzer"]
    end

    subgraph Providers["Provider Implementations"]
        direction LR
        P_OAI["OpenAI"]
        P_ANT["Anthropic"]
        P_GEM["Gemini"]
        P_MIS["Mistral"]
        P_GRQ["Groq"]
        P_BED["Bedrock"]
        P_MORE["...20+ more"]
    end

    subgraph MJLayer["MJ-Integrated Layer  (Requires MJ Runtime)"]
        ENG["AI Engine"]
        PRM["Prompt Runner"]
        AGT["Agent Framework"]
        VEC["Vector Sync"]
        MCP["MCP Server"]
        A2A["A2A Server"]
    end

    APP --> Core
    APP --> MJLayer
    Core --> Providers
    MJLayer --> Core

    style Core fill:#1a365d,stroke:#2b6cb0,color:#fff
    style Providers fill:#2d6a9f,stroke:#4299e1,color:#fff
    style MJLayer fill:#553c9a,stroke:#805ad5,color:#fff
    style Application fill:#2d8659,stroke:#48bb78,color:#fff

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Provider Capabilities

Each provider implements one or more capability interfaces from @memberjunction/ai. The table below shows what each provider supports.

Provider	npm Package	LLM	Embeddings	Image Gen	Audio	Video	Reranking
OpenAI	@memberjunction/ai-openai	x	x	x	x
Anthropic	@memberjunction/ai-anthropic	x
Google Gemini	@memberjunction/ai-gemini	x	x	x
Mistral	@memberjunction/ai-mistral	x	x
Groq	@memberjunction/ai-groq	x
xAI (Grok)	@memberjunction/ai-xai	x
Azure AI	@memberjunction/ai-azure	x	x
Amazon Bedrock	@memberjunction/ai-bedrock	x	x
Google Vertex	@memberjunction/ai-vertex	x
Fireworks	@memberjunction/ai-fireworks	x
OpenRouter	@memberjunction/ai-openrouter	x
Cerebras	@memberjunction/ai-cerebras	x
MiniMax	@memberjunction/ai-minimax	x
Zhipu (Z.AI)	@memberjunction/ai-zhipu	x
Ollama	@memberjunction/ai-ollama	x	x
LM Studio	@memberjunction/ai-lmstudio	x
BettyBot	@memberjunction/ai-betty-bot	x
Black Forest Labs	@memberjunction/ai-blackforestlabs			x
ElevenLabs	@memberjunction/ai-elevenlabs				x
HeyGen	@memberjunction/ai-heygen					x
Cohere	@memberjunction/ai-cohere		x				x
Local Embeddings	@memberjunction/ai-local-embeddings		x
Pinecone	@memberjunction/ai-vectors-pinecone
Rex (rasa.io)	@memberjunction/ai-recommendations-rex

Pinecone and Rex implement vector database and recommendation interfaces respectively, not the base AI model interfaces.

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Package Directory

Core Abstractions

Package	npm	Standalone	Description
Core	@memberjunction/ai	Yes	Base classes (BaseLLM, BaseEmbeddings, BaseImageGenerator, BaseAudio, BaseVideo, BaseReranker), type definitions, error analysis, API key management
CorePlus	@memberjunction/ai-core-plus		Extended AI types that reference MJ entity concepts; shared by server and client
BaseAIEngine	@memberjunction/ai-engine-base	Yes	Base engine class with extended types and data caching
Reranker	@memberjunction/ai-reranker		AI reranker service with LLM-based two-stage retrieval

Orchestration and Engines

Package	npm	Standalone	Description
Engine	@memberjunction/aiengine		AI orchestration engine — automatic execution of Entity AI Actions using configured models
Prompts	@memberjunction/ai-prompts		Prompt execution engine with hierarchical template composition, system placeholders, parallel runs, and output validation
Agents	@memberjunction/ai-agents		Agent execution and management with metadata-driven agent types, sub-agent delegation, and client tool invocation for browser-side UI operations

Agent Management

Package	npm	Standalone	Description
AgentManager/core	@memberjunction/ai-agent-manager		Core interfaces, types, and AgentSpec for agent metadata management
AgentManager/actions	@memberjunction/ai-agent-manager-actions		Agent management actions (create, update, list, deactivate, export)

Protocol Servers

Package	npm	Standalone	Description
MCPServer	@memberjunction/ai-mcp-server		Model Context Protocol server exposing MJ entity CRUD and agent execution to MCP-compatible clients
MCPClient	@memberjunction/ai-mcp-client		MCP client for consuming external MCP tool servers
A2AServer	@memberjunction/a2aserver		Agent-to-Agent (A2A) protocol server for cross-system agent interoperability

Vector Operations

Package	npm	Standalone	Description
Vectors/Core	@memberjunction/ai-vectors		Core vector operations and abstractions for entity vectorization
Vectors/Database	@memberjunction/ai-vectordb		Vector database abstraction layer (index management, query operations)
Vectors/Sync	@memberjunction/ai-vector-sync		Synchronization between MJ entities and vector databases
Vectors/Dupe	@memberjunction/ai-vector-dupe		Duplicate record detection using vector similarity
Vectors/Memory	@memberjunction/ai-vectors-memory		In-memory vector utilities

Recommendations

Package	npm	Standalone	Description
Recommendations/Engine	@memberjunction/ai-recommendations		Provider-agnostic recommendation engine with run tracking and entity integration

Computer Use

Package	npm	Standalone	Description
ComputerUse	@memberjunction/computer-use	Yes	Vision-to-action browser automation engine for LLM-driven web interactions
MJComputerUse	@memberjunction/computer-use-engine		MJ-integrated computer use engine with entity-backed configuration

CLI

Package	npm	Standalone	Description
AICLI	@memberjunction/ai-cli		AI agent, prompt, and action execution CLI integrated with the MJ CLI

Provider Implementations

All 25 provider packages are listed in the Providers README. The meta-package @memberjunction/ai-provider-bundle imports all standard providers to prevent tree-shaking in bundled applications.

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Quick Start

1. Install

# Core + one provider (pick any from the table above)
npm install @memberjunction/ai @memberjunction/ai-openai

2. Set Your API Key

export AI_VENDOR_API_KEY__OPENAILLM=sk-...

Or pass the key directly when constructing the provider.

3. Chat

import { ChatParams, ChatMessageRole } from "@memberjunction/ai";
import { OpenAILLM } from "@memberjunction/ai-openai";

const llm = new OpenAILLM(process.env.AI_VENDOR_API_KEY__OPENAILLM!);

const result = await llm.ChatCompletion({
    model: "gpt-4.1",
    messages: [{ role: ChatMessageRole.user, content: "Hello, world!" }],
});

console.log(result.data.choices[0].message.content);

4. Stream

await llm.ChatCompletion({
    model: "gpt-4.1",
    messages: [{ role: ChatMessageRole.user, content: "Write a haiku about TypeScript." }],
    streaming: true,
    streamingCallbacks: {
        OnContent: (chunk) => process.stdout.write(chunk),
        OnComplete: () => console.log(),
    },
});

5. Embed

import { OpenAIEmbedding } from "@memberjunction/ai-openai";

const embedder = new OpenAIEmbedding(process.env.AI_VENDOR_API_KEY__OPENAILLM!);
const result = await embedder.EmbedText({
    text: "Semantic search is powerful",
    model: "text-embedding-3-small",
});
console.log(`Vector dimensions: ${result.vector.length}`);

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Switching Providers

Because all providers implement the same BaseLLM interface, switching is a one-line change:

// Before: OpenAI
import { OpenAILLM } from "@memberjunction/ai-openai";
const llm = new OpenAILLM(openaiKey);

// After: Anthropic
import { AnthropicLLM } from "@memberjunction/ai-anthropic";
const llm = new AnthropicLLM(anthropicKey);

// After: Local (Ollama)
import { OllamaLLM } from "@memberjunction/ai-ollama";
const llm = new OllamaLLM(""); // No API key needed

Your ChatCompletion() calls, streaming callbacks, and error handling remain identical.

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Creating a New Provider

Extend the appropriate base class and register it with the class factory:

import { BaseLLM, ChatParams, ChatResult } from "@memberjunction/ai";
import { RegisterClass } from "@memberjunction/global";

@RegisterClass(BaseLLM, "MyCustomLLM")
export class MyCustomLLM extends BaseLLM {
    protected async nonStreamingChatCompletion(params: ChatParams): Promise<ChatResult> {
        // Call your provider API here
    }

    // For streaming, override SupportsStreaming + streaming hooks
}

If your provider uses an OpenAI-compatible API, extend OpenAILLM instead and override the base URL. See the Groq or xAI providers for examples.

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API Key Management

The framework resolves API keys through environment variables using the convention:

AI_VENDOR_API_KEY__<DRIVER_CLASS>

For example:

• AI_VENDOR_API_KEY__OPENAILLM for OpenAI
• AI_VENDOR_API_KEY__ANTHROPICLLM for Anthropic
• AI_VENDOR_API_KEY__GEMINILLM for Google Gemini

You can also pass keys directly to provider constructors or supply runtime overrides via GetAIAPIKey().

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Error Handling

All providers use a unified ErrorAnalyzer that classifies failures into structured error types:

const result = await llm.ChatCompletion(params);
if (!result.success) {
    console.error(result.errorMessage);
    // result.exception contains the original error
    // ErrorAnalyzer provides: errorType, severity, isRetryable, failoverRecommended
}

Error types include rate limiting, authentication failures, context length exceeded, content filtering, and more — enabling intelligent retry and failover logic.

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Further Reading

• Core Package README — Full API reference for base classes, types, and utilities
• Providers README — Complete provider listing with categories
• Prompts Package — Hierarchical prompt template system
• Agents Package — Agent framework and sub-agent delegation
• MCP Server — Expose MJ capabilities via Model Context Protocol
• A2A Server — Agent-to-Agent interoperability
• Computer Use — Vision-driven browser automation
• MemberJunction Documentation — Full platform documentation

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License

See the MemberJunction repository root for license details.