⚡BoxLang AI Module Documentation

Welcome to the BoxLang AI Module - your unified gateway to integrating AI capabilities into BoxLang applications. This module provides an elegant, easy-to-use API for interacting with multiple AI providers, from simple chat requests to complex multi-agent systems.

🎯 What is BoxLang AI?

BoxLang AI is a comprehensive module that brings enterprise-grade artificial intelligence capabilities to the BoxLang ecosystem. Whether you're building chatbots, content generators, code assistants, RAG systems, or complex AI workflows, this module provides everything you need.

✨ Key Features

🌐 Multi-Provider Support: Work with OpenAI, Claude, Gemini, Grok, Groq, DeepSeek, Ollama, and more
🔄 Unified API: One consistent interface across all providers
👥 Multi-Tenant Memory: Enterprise-grade isolation with userId and conversationId across all 20 memory types
🎨 Multimodal Content: Process images, audio, video, and documents alongside text
🏠 Local AI Support: Run models locally with Ollama for privacy and offline use
🔗 AI Pipelines: Chain operations together for complex multi-step workflows
⚡ Streaming Responses: Get real-time responses as they're generated
🛠️ Tool Integration: Enable AI to call functions and access real-time data
🚀 Async Support: Non-blocking operations for better performance
📝 Template System: Create reusable prompts with dynamic placeholders
🤖 AI Agents: Autonomous agents with memory, tools, and reasoning
📄 Document Loaders: Load and process various file formats for RAG
🧠 Vector Memory: Semantic search with 10+ vector database integrations

Supported Providers

BoxLang supports out of the box with a variety of AI providers. You can also create custom providers by following our Custom Provider Guide.

Provider

Type

Best For

OpenAI

Cloud

General purpose, GPT-5, etc

Claude

Cloud

Long context, detailed analysis

Gemini

Cloud

Google integration, multimodal

Grok

Cloud

Real-time data, Twitter integration

HuggingFace

Cloud

Open-source models, community-driven

Groq

Cloud

Ultra-fast inference, LPU architecture

DeepSeek

Cloud

Code generation, reasoning

Ollama

Local

Privacy, offline use, no API costs

OpenRouter

Gateway

Access multiple models through one API

Perplexity

Cloud

Research, citations, factual answers

Voyage

Cloud

State-of-the-art embeddings, specialized for RAG

Cohere

Cloud

Embeddings, multilingual, chat, tool calling

🚀 Use Cases

💬 Chatbots: Build conversational interfaces with memory and context
✍️ Content Generation: Create articles, documentation, marketing copy
💻 Code Assistance: Generate, review, and explain code
📊 Data Analysis: Extract insights from text and structured data
📄 Document Processing: Analyze PDFs, contracts, and reports
🎥 Media Analysis: Process images, audio, and video content
🌍 Translations: Multi-language content translation
📋 Summarization: Condense long documents intelligently
❓ Question Answering: Build knowledge bases and FAQs with RAG
🔄 Custom Workflows: Multi-step AI processing pipelines

📖 Documentation Structure

🎓 Getting Started

Perfect for beginners - get up and running quickly

💬 Simple AI Interactions

Learn basic chat, streaming, and structured output

🔗 AI Pipelines

Build complex workflows with agents, memory, and tools

🔬 Advanced Topics

Deep dives into specialized features and customization

📋 Table of Contents

🎓 Getting Started

Get started with BoxLang AI quickly and easily: Overview

🎓 AI Concepts Overview of AI concepts, terminology, and architecture.

📦 Installation Install the BoxLang AI module in minutes.

🧩 Provider Setup & Configuration Detailed setup for all 12+ AI providers with API keys, models, and best practices.

⚡ Quick Start Guide Get up and running in minutes with simple examples and your first AI chat.

🙋FAQ Common questions and troubleshooting tips for BoxLang AI.

💬 Simple AI Interactions

Get started quickly with BoxLang AI's core chat capabilities: Overview

🗣️ Basic Chatting Simple question-answer interactions, parameters, and provider switching.

🎯 Advanced Chatting Multi-message conversations, AI tools, async requests, and streaming responses.

⚙️ Service-Level Chatting Direct service control, custom requests, headers, and managing multiple providers.

📦 Structured Output Extract type-safe, validated data from AI responses using classes, structs, or schemas.

🔗 AI Pipelines & Components

✈️ Main Components Overview Core concepts of AI pipelines, composability, and building workflows.

🔄 Understanding Pipelines Core concepts of AI pipelines, composability, and building workflows.

🤖 AI Agents Create autonomous agents with memory, tools, and reasoning. Simplify complex AI workflows.

🧠 Working with Models Creating model runnables, configuration, and integrating AI providers into pipelines.

✉️ Message Templates Building reusable prompts with dynamic placeholders and binding strategies.

🛠️ AI Tools & Function Calling Enable AI to call functions, access real-time data, and interact with external systems.

💭 Memory Systems Maintain conversation context with Windowed, Summary, Session, File, Cache, and JDBC memory.

🔮 Vector Memory Semantic search using vector embeddings. Integrate ChromaDB, Pinecone, PostgreSQL, and more.

🔧 Transformers Process and transform data between pipeline steps with pre/post-processing.

🏗️Structured Output Extract type-safe, validated data from AI responses using classes, structs, or schemas.

🔗 Pipelines Build composable AI workflows by chaining models, messages, and transformers into reusable templates.

📡 Pipeline Streaming Real-time streaming through pipelines for responsive applications.

📄 Document Loaders Load documents from files, directories, URLs. Supports text, Markdown, CSV, JSON, XML, and more.

🎯 RAG (Retrieval Augmented Generation) Build RAG systems combining document loaders, vector memory, and AI models.

🔬 Advanced Topics

🔐 Message Context Inject security, RAG, and application context into AI messages with multi-tenant patterns.

🎪 Event System Intercept and customize AI operations with hooks for monitoring, security, and extensibility.

🔢 Embeddings Generate vector representations for semantic search, recommendations, and similarity detection.

👥 Multi-Tenant Memory Enterprise-grade memory isolation with userId and conversationId patterns.

🛠️ Utility Functions Text chunking, token counting, and optimization techniques for AI processing.

🔌 MCP Client Connect to Model Context Protocol servers for external tools, resources, and prompts.

🖥️ MCP Server Expose BoxLang capabilities as MCP server for integration with other AI systems.

🦾Production Deployments Best practices for deploying BoxLang AI applications in production environments.

🔰Security & Compliance Guidelines and best practices for securing AI applications and ensuring compliance.

🎨 Custom AI Providers Build custom provider integrations to connect any LLM service with BoxLang AI.

🧠 Custom Memory Build your own memory implementations by extending BaseMemory.

🧩 Custom Vector Memory Implement custom vector memory providers by extending BaseVectorMemory.

📚 Custom Document Loaders Create custom loaders for specialized data sources and formats.

🔄 Custom Transformers Build custom transformers for specialized data processing in pipelines.

🔧 Built-In Functions (BIFs)

BoxLang AI provides a comprehensive set of BIFs for different AI operations. You can see all of our BIF reference documentation here: BIF Reference.

💬 Chat & Conversation

BIF

Purpose

Return Type

Example Use Case

aiChat()

Simple one-shot chat request

String

Quick Q&A, content generation

aiChatAsync()

Non-blocking chat request

Future

Background processing, parallel requests

aiChatRequest()

Build structured chat requests

AiRequest

Complex requests with tools

aiChatStream()

Real-time streaming responses

void

Live chat, progressive output

🏗️ Pipeline Components

BIF

Purpose

Return Type

Example Use Case

aiMessage()

Build message pipelines

AiMessage

Reusable prompts, templates

aiModel()

Create model runnables

AiModel

Pipeline integration

aiTransform()

Create data transformers

Transformer

Pipeline data processing

aiTool()

Define callable functions

Tool

Real-time data, function calling

🧠 Memory & Context

BIF

Purpose

Return Type

Example Use Case

aiMemory()

Create conversation or vector memory

Memory

Context-aware conversations, RAG, semantic search

📄 Document Processing

BIF

Purpose

Return Type

Example Use Case

aiDocuments()

Load documents from sources

Array/Loader

Document processing, RAG

🔢 Utilities

BIF

Purpose

Return Type

Example Use Case

aiChunk()

Split text into chunks

Array

Processing large documents

aiTokens()

Estimate token counts

Numeric

Cost estimation, limits

aiEmbed()

Generate vector embeddings

Array/Struct

Semantic search, similarity

⚙️ Service Management

BIF

Purpose

Return Type

Example Use Case

aiService()

Get AI service instances

Service

Multi-provider management

MCP()

Connect to MCP servers

MCPClient

External tools, resources

Quick Reference by Category:

🚀 Simple Operations: aiChat(), aiChatAsync(), aiChatStream()
📝 Structured Requests: aiChatRequest(), aiMessage(), aiModel()
🔧 Advanced Features: aiTool(), aiMemory(), aiTransform()
📊 Utilities: aiChunk(), aiTokens(), aiEmbed()
🎛️ Service Management: aiService(), MCP()

🚀 Quick Examples

💬 Simple Chat

answer = aiChat( "What is BoxLang?" )
println( answer )

🎨 Simple Chat with Parameters

answer = aiChat(
    "Write a haiku about coding",
    { temperature: 0.9, model: "gpt-4" }
)

🔗 Build a Pipeline

pipeline = aiMessage()
    .system( "You are a helpful assistant" )
    .user( "Explain ${topic}" )
    .toDefaultModel()
    .transform( r => r.content )

result = pipeline.run( { topic: "recursion" } )

⚡ Stream Responses

aiChatStream(
    "Tell me a story",
    ( chunk ) => print( chunk.choices?.first()?.delta?.content ?: "" )
)

📦 Get JSON Responses

// Automatically parse JSON responses
user = aiChat(
    "Create a user profile with name, age, and email for Alice",
    { returnFormat: "json" }
)

println( "Name: #user.name#" )
println( "Age: #user.age#" )
println( "Email: #user.email#" )

🛠️ Use AI Tools

// Let AI call functions for real-time data
getWeather = aiTool(
    name: "get_weather",
    description: "Get current weather for a location",
    parameters: {
        location: { type: "string", description: "City name" }
    },
    callback: ( args ) => {
        return { temp: 72, condition: "sunny", location: args.location }
    }
)

response = aiChat(
    "What's the weather in San Francisco?",
    { tools: [ getWeather ] }
)

🔢 Generate Embeddings

// Create vector embeddings for semantic search
embeddings = aiEmbed([
    "BoxLang is a modern JVM language",
    "Java is a programming language",
    "Python is popular for AI"
])

// Use embeddings for similarity comparison
println( "Generated #embeddings.len()# embeddings" )

📄 Load Documents

// Load documents for RAG
documents = aiDocuments( source: "docs/guide.md" )

// Use with memory
memory = aiMemory( type: "vector" )
memory.addDocuments( documents )

// Query with context
response = aiChat(
    "What is covered in the guide?",
    { memory: memory }
)

🤖 Create an Agent

// Build an autonomous agent
agent = aiAgent()
    .name( "Research Assistant" )
    .instructions( "You help research and summarize topics" )
    .memory( aiMemory( type: "windowed", size: 10 ) )
    .tools([
        searchTool,
        summarizeTool
    ])

// Agent handles multi-turn conversations
response = agent.chat( "Research AI trends in 2025" )

🆘 Need Help?

📚 Resources

📖 Full Documentation: Explore all sections above for comprehensive guides
💡 Examples: Check the /examples folder for runnable code samples
🔍 BIF Reference: See reference/built-in-functions/ for detailed function docs
📦 Module Components: Explore main-components/ for in-depth component guides

🤝 Community & Support

👥 Community: BoxLang Community Forum
🐛 Issues: GitHub Issues
💬 Discussions: GitHub Discussions
✉️ Email Support: [email protected]

🎓 Learning Paths

🌱 Beginners: Start with Quick Start → Basic Chatting → Examples
🏗️ Builders: Learn Pipelines → Memory → Tools
🚀 Advanced: Explore Agents → RAG → Custom Components

🌟 Upgrade to Plus

Want enterprise features and priority support?

🏢 Enterprise Modules: Advanced components and integrations
🛠️ Advanced Tooling: Enhanced development and debugging tools
⚡ Priority Support: Direct access to our engineering team
🔐 Enterprise Features: SSO, audit logs, advanced security

Learn more: boxlang.io/plans

📜 Legal & Credits

Made with ❤️ by the Ortus Solutions team

NextAdvanced Topics

Last updated 1 hour ago

Good afternoon

🎯 What is BoxLang AI?

✨ Key Features

Supported Providers

🚀 Use Cases

📖 Documentation Structure

🎓 Getting Started

💬 Simple AI Interactions

🔗 AI Pipelines

🔬 Advanced Topics

📋 Table of Contents

🎓 Getting Started

💬 Simple AI Interactions

🔗 AI Pipelines & Components

🔬 Advanced Topics

🔧 Built-In Functions (BIFs)

💬 Chat & Conversation

🏗️ Pipeline Components

🧠 Memory & Context

📄 Document Processing

🔢 Utilities

⚙️ Service Management

🚀 Quick Examples

💬 Simple Chat

🎨 Simple Chat with Parameters

🔗 Build a Pipeline

⚡ Stream Responses

📦 Get JSON Responses

🛠️ Use AI Tools

🔢 Generate Embeddings

📄 Load Documents

🤖 Create an Agent

🆘 Need Help?

📚 Resources

🤝 Community & Support

🎓 Learning Paths

🌟 Upgrade to Plus

📜 Legal & Credits