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Juggernaut Labs Documentation

Juggernaut Labs is a code-first operating system for automating internal business processes using Large Language Models (LLMs). Unlike visual workflow builders, Juggernaut provides a declarative JSON API for defining complex decision trees, combined with React SDK interfaces and a TypeScript Plugin SDK for extensibility.

Core Philosophy

Processes as Code: Business logic is defined through structured JSON schemas (InputProcess API), version-controlled, and deployed programmatically. This enables:

  • Reproducibility: Same input always produces same execution path
  • Collaboration: Technical and non-technical stakeholders contribute via prompt engineering and schema design
  • Integration: Native connection to databases, APIs, and external systems through type-safe plugins

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    INPUTPROCESS API                          │
│              (JSON Workflow Definitions)                     │
└──────────────────────┬──────────────────────────────────────┘
                       │
         ┌─────────────▼──────────────┐
         │      PIPELINES[]         │  ← Sequential execution array
         │   (Sync/Async/Conditional)│
         └─────────────┬──────────────┘
                       │
              ┌────────▼────────┐
              │     STEPS[]     │  ← Individual AI tasks
              │  (LLM/Plugin)   │
              └────────┬────────┘
                       │
         ┌─────────────▼──────────────┐
         │      @juggernautlabs/views │  ← React SDK for interfaces
         │      @juggernautlabs/plugins│  ← TypeScript plugin SDK
         └────────────────────────────┘

Key Components

1. InputProcess API

The declarative engine. You define workflows as JSON objects containing:

  • Pipelines: Execution phases (array order = execution order)
  • Steps: Individual tasks with LLM prompts or plugin calls
  • State Management: Type-safe variable passing between steps

2. Views SDK (@juggernautlabs/views)

React toolkit for building Process-Driven Applications—interfaces that consume process outputs. Features:

  • Automatic job data injection via hooks
  • Type-safe output consumption
  • Vibe-coding ready (standard React patterns)

3. Plugins SDK (@juggernautlabs/plugins)

TypeScript framework for extending capabilities:

  • Database connectors (MongoDB, Postgres, etc.)
  • External API integrations
  • Custom business logic execution
  • State modification during process runs

Workflow Lifecycle

  1. Define: Write InputProcess JSON specifying inputs, pipelines, steps, and output schemas
  2. Upload: Deploy process definition via API
  3. Execute: Trigger via API call or SDK with runtime inputs
  4. Interface: Build React view using @juggernautlabs/views to display results
  5. Extend: Develop plugins using @juggernautlabs/plugins for custom integrations

When to Use Juggernaut

Ideal for:

  • Multi-step AI workflows requiring structured output at each phase
  • Processes needing conditional logic (if X > 0.8, route to specialist pipeline)
  • Bulk operations (iterating over arrays with AI processing per item)
  • Applications requiring type-safe database integration
  • Teams wanting version-controlled business logic

Not a replacement for:

  • Simple single-prompt chatbots (overkill)
  • Visual drag-and-drop automation (Zapier/Make territory—though Juggernaut can orchestrate those via plugins)

Next Steps