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bx-ai-pipelines

Chain aiMessage(), aiModel(), and aiTransform() steps into deterministic BoxLang pipelines.

Updated Apr 10, 2026
Search Tags:#pipeline#multimodel#boxlang#aimessage#aimodel#aitransform
One-click install
npx skills add https://github.com/ortus-boxlang/skills --skill bx-ai-pipelines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bx-ai-pipelines
Source: https://github.com/ortus-boxlang/skills/tree/main/boxlang-modules/bx-ai/bx-ai-pipelines
Command: npx skills add https://github.com/ortus-boxlang/skills --skill bx-ai-pipelines

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables developers to compose and reuse AI-driven pipelines in BoxLang, linking message templating, model calls, and transforms into cohesive workflows.

Core Features & Use Cases

  • Composable Pipelines: Chain aiMessage(), aiModel(), and aiTransform() steps into reusable workflows.
  • Multi-Model Orchestration: Build pipelines that switch models per step and stream results.
  • Production Readiness: Supports streaming, structured output, and immutability for safe reuse.

Quick Start

Create a pipeline that translates input text by composing aiMessage(), toDefaultModel(), and aiTransform() steps to produce a final string.

Frequently Asked Questions about bx-ai-pipelines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build reusable AI pipelines for multi-model workflows?▼

Build reusable AI pipelines by chaining aiMessage(), aiModel(), and aiTransform() steps into deterministic workflows to orchestrate multi-model translation, summarization, and data transformation tasks.

Can I switch AI models per step in a streaming pipeline?▼

Yes, multi-model orchestration allows switching AI models per step within streaming pipelines, supporting structured outputs and immutability for safe reuse across multi-model scenarios.

How do I chain message templating and model calls in BoxLang?▼

Chain message templating and model calls in BoxLang by composing aiMessage() for templating, aiModel() for calls, and aiTransform() for data transformation into cohesive workflows.

Does BoxLang support deterministic AI workflows with error handling?▼

Yes, BoxLang supports deterministic AI workflows with built-in error handling, modularity, and immutability, ensuring safe reuse and compatibility with the BoxLang runtime and bx-ai components.

What is the best way to format structured output from multiple AI models?▼

Format structured output from multiple AI models by orchestrating multi-model pipelines that apply aiTransform() steps to produce deterministic, structured results for data transformation tasks.

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