AxilJS AI Platform Overview

The AxilJS AI platform provides a unified foundation for building AI applications with TypeScript and Node.js.

It brings together LLM provider abstraction, streaming, embeddings, vector search, retrieval-augmented generation (RAG), AI agents, and Model Context Protocol (MCP) integration under the @axiljs/ai package.

The provider abstraction lets applications work with different AI providers while keeping application-level AI code consistent.

What Is Included#

AxilJS AI includes the following capabilities:

• Provider abstraction — Connect to OpenAI, Anthropic, Gemini, and Ollama through a unified interface.
• Streaming — Process AI responses token-by-token using streaming and SSE.
• Embeddings — Convert text into numerical vectors for semantic search and retrieval workflows.
• Vector store — Store and retrieve vectors for semantic search.
• RAG pipeline — Index documents and query relevant information for AI applications.
• Agents — Build ReAct-style AI agents that can reason and use tools.
• MCP client — Connect AI applications and agents to external tools through Model Context Protocol.

These capabilities can be used independently or combined to build more advanced AI workflows.

Quick Example#

The @axiljs/ai package provides a common interface for interacting with supported AI providers.

For example, you can use the Ollama provider with a local model:

import { OllamaProvider } from '@axiljs/ai'
 
const ai = new OllamaProvider({
  model: 'llama3.2'
})
 
const res = await ai.complete({
  messages: [
    {
      role: 'user',
      content: 'What is AxilJS?'
    }
  ]
})
 
console.log(res.content)

The same completion interface can be used across the supported provider implementations.

Switch LLM Providers#

AxilJS separates application-level AI logic from the underlying LLM provider.

This means you can switch providers without rewriting the application code that consumes the AI interface.

Application
     │
     ▼
@axiljs/ai
     │
     ▼
Provider Abstraction
     │
 ┌───┼───────────┬──────────┐
 ▼   ▼           ▼          ▼
OpenAI Anthropic Gemini   Ollama

Provider selection can be changed through environment configuration, allowing the underlying model provider to change without requiring changes to the application-level AI logic.

AI Platform Architecture#

The main AxilJS AI capabilities can be viewed as layers:

                    AxilJS AI
                       │
        ┌──────────────┼──────────────┐
        ▼              ▼              ▼
    Providers      AI Runtime       Tools
        │              │              │
        ▼              ▼              ▼
   LLM Models      Streaming       Agents
                       │              │
                       ▼              ▼
                  Embeddings        MCP
                       │
                       ▼
                 Vector Store
                       │
                       ▼
                      RAG

Each capability addresses a different part of an AI application:

• Providers handle access to LLMs.
• Streaming handles incremental AI responses.
• Embeddings convert text into vectors.
• Vector stores support semantic retrieval.
• RAG combines retrieval with AI generation.
• Agents combine model reasoning with executable tools.
• MCP provides an interface for integrating external tools.

Build AI Applications with AxilJS#

The platform can be used for different levels of AI application complexity.

For a basic AI application, start with a provider and complete():

const res = await ai.complete({
  messages: [
    {
      role: 'user',
      content: 'What is AxilJS?'
    }
  ]
})

For applications that need semantic retrieval, use embeddings and the RAG capabilities.

For applications that need autonomous multi-step tool execution, use Agents.

For applications that need external tool integration, use the MCP client.

Common Use Cases#

AxilJS AI capabilities can be combined to build applications such as:

• AI chat applications
• AI assistants
• Semantic search systems
• Retrieval-augmented generation applications
• Document question-answering systems
• Tool-enabled AI agents
• Multi-step AI workflows
• Applications using local LLMs
• Applications integrating external MCP tools

Explore the AI Platform#

Start with Providers to understand the LLM provider abstraction.

Then explore Embeddings and RAG for retrieval-based AI applications.

For tool-enabled AI workflows, see Agents and MCP.