multi-agent-pipeline

Name: multi-agent-pipeline
Availability: InStock
Author: pixl

Coordinate domain-specific LLM agents with an orchestrator and runner for structured outputs.

2|Updated Mar 16, 2026

Search Tags:#llm #multi-agent #agents #orchestration #pipeline #domain-specific

Ready to run

npx skills add https://github.com/hamzaPixl/pixl-ai --skill multi-agent-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-agent-pipeline
Source: https://github.com/hamzaPixl/pixl-ai/tree/main/packages/crew/skills/multi-agent-pipeline
Command: npx skills add https://github.com/hamzaPixl/pixl-ai --skill multi-agent-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrate multiple domain-specific LLM agents with a centralized orchestrator and runner to deliver coordinated, structured outputs for complex product features.

Core Features & Use Cases

Base agent class and domain-specific agents with clear contracts and prompts
Orchestrator and runner that coordinate parallel or sequential agent execution and aggregate results
Deterministic, structured output suitable for downstream pipelines and analytics
Use Case: Build a feature that requires consulting domain experts (e.g., pricing, user intent, and risk assessment) and synthesize their outputs into a single decision.

Quick Start

Instantiate the multi-agent pipeline scaffold and run the orchestrator with your domain agents to boot the workflow.

Frequently Asked Questions about multi-agent-pipeline

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

FAQPage Schema

How do I orchestrate multiple LLM agents to produce a single structured output?▼

You orchestrate multiple LLM agents by deploying a centralized orchestrator and runner that coordinate domain-specific agents in parallel or sequentially, aggregating their results into deterministic structured outputs for downstream pipelines.

When do I need a multi-agent pipeline for my product feature?▼

You need a multi-agent pipeline when a product feature requires consulting multiple domain experts, such as pricing, user intent, and risk assessment, and synthesizing their separate outputs into a single coordinated decision.

How does an orchestrator coordinate domain agents in parallel or sequentially?▼

The orchestrator coordinates domain agents by utilizing a base agent contract and runner logic to execute agents either in parallel or sequentially, applying logging and deterministic output formatting to aggregate structured results.

Can I use domain-specific agents with custom contracts and prompts in an LLM pipeline?▼

Yes, you can implement domain-specific agents by extending a base agent class that enforces clear contracts and prompts, allowing the orchestrator to reliably manage and execute each specialized agent within the pipeline.

What is the best way to structure LLM agent outputs for downstream analytics?▼

The best way to structure LLM agent outputs for analytics is to use a pipeline runner that applies deterministic output formatting, ensuring the aggregated results from all domain agents are structured consistently for downstream consumption.

Do I need external dependencies to build a multi-agent LLM orchestration pipeline?▼

No external dependencies are required to build the multi-agent orchestration pipeline, as it implements the base agent contract, orchestrator logic, runner, and logging internally to support end-to-end workflows.