Inworld Runtime CLI

The Inworld Runtime CLI is a comprehensive command-line toolkit that streamlines the entire lifecycle of Runtime graph development - from initial prototyping to production deployment and continuous optimization. To download and install Inworld Runtime CLI, run the following command:

npm

npm install -g @inworld/runtime

Commands

The CLI follows a logical command hierarchy that matches developer workflows:

inworld-runtime
├── auth          # Authentication
│   ├── login     # User authentication (alias: inworld-runtime login)
│   ├── logout    # Sign out (alias: inworld-runtime logout)
│   ├── print-access-token  # Display access token
│   ├── print-api-key       # Display API key
│   └── status    # Show authentication status (alias: inworld-runtime status)
├── init          # Project creation with templates  
├── graph         # Graph management and deployment
│   ├── run       # Local testing and development (alias: inworld-runtime run)
│   ├── serve     # Local HTTP/gRPC servers (alias: inworld-runtime serve)
│   ├── deploy    # Cloud deployment (alias: inworld-runtime deploy)
│   ├── visualize # Generate architectural diagrams
│   └── variant   # A/B testing and experimentation
├── tts           # Text-to-speech synthesis
└── workspace     # Workspace management

Project Initialization

The inworld-runtime init command downloads the llm-to-tts-node template—a production-ready LLM to TTS pipeline.

Currently, only the llm-to-tts-node template is available via CLI. CLI support for fetching additional templates is coming soon. To view all available templates, visit the templates repository.

For graph-only examples that demonstrate specific Runtime features, clone the templates repository directly.

# Initialize with the llm-to-tts-node template
inworld-runtime init --template llm-to-tts-node --name my-ai-app

# Skip dependency installation prompt
inworld-runtime init --template llm-to-tts-node --name my-ai-app --skip-install

# Force refresh template cache (download fresh copy even if cached)
inworld-runtime init --template llm-to-tts-node --name my-ai-app --force

Init Command Options:

-n, --name <name> - Name of the project directory
-t, --template <template> - Template to use (currently only llm-to-tts-node)
--skip-install - Skip dependency installation prompt
--force - Force refresh template cache (download fresh copy even if cached)

Working with Graphs

Once you have a graph.ts file (either from a template project or created manually), you can use the following commands to work with your graph:

Local Testing

Test your graph locally with instant feedback:

# Test locally with instant feedback
inworld-runtime run ./graph.ts '{"input": {"user_input": "test message"}}'

Visualization

Generate architectural diagrams of your graph:

# Visualize complex graph architectures  
inworld-runtime graph visualize ./graph.ts --output ./docs/architecture.png

Graph visualization requires Graphviz to be installed on your system.

Local Server

Serve your graph locally with support for both HTTP (with optional Swagger UI) and gRPC:

# Serve on HTTP Server with Swagger
inworld-runtime serve ./graph.ts --swagger  

# Serve on custom port
inworld-runtime serve ./graph.ts --port 8080

# Serve on custom host
inworld-runtime serve ./graph.ts --host 0.0.0.0 --port 3000

# Combine options
inworld-runtime serve ./graph.ts --host 0.0.0.0 --port 8080 --swagger

# gRPC server for high-performance applications
inworld-runtime serve ./graph.ts --transport grpc 

Cloud Deployment

Once you’ve successfully built your graph, you can deploy it to Inworld Cloud to create a persistent, production-ready endpoint. See Cloud Deployment for more details.

# Deploy to cloud with persistent endpoint
inworld-runtime deploy ./graph.ts

A/B Testing & Experimentation

You can use the CLI to create and manage graph variants, such as model or prompt changes, for A/B testing and experimentation. Your clients will automatically use the latest variants—no client-side updates required.

# Register different variants for testing
inworld-runtime graph variant register -d baseline ./graph-v1.ts
inworld-runtime graph variant register -d experiment ./graph-v2.ts

# Configure traffic splits in Portal, monitor results

TTS

Use the CLI to quickly get started with Inworld TTS. Generate speech content with direct TTS commands:

# Direct TTS synthesis for immediate use
inworld-runtime tts synthesize "Hello world" --voice Dennis --output audio.mp3

To create graphs that use TTS, initialize a template project with inworld-runtime init --template llm-to-tts-node (see Project Initialization above).

Telemetry

The Inworld Runtime CLI collects general usage data to help us improve the tool. Information such as the following are tracked:

Command execution time
CLI version
Command name

Managing Telemetry Inworld takes privacy and security seriously. You can opt-out if you prefer not to share any telemetry information.

# Disable telemetry collection
inworld-runtime auth disable_telemetry

# Enable telemetry collection
inworld-runtime auth enable_telemetry

Next Steps

Ready to start building with the Inworld Runtime CLI?

Quickstart

Guide to installation, authentication, and first project setup

Deployment Guide

Deploy your graphs to production with persistent endpoints

Need Help?

Troubleshooting Guide - Common issues and solutions
CLI Help: Run inworld-runtime help or inworld-runtime [command] --help for detailed command information

Get Started

Build with Agent Runtime

Deploy

Monitor & Experiment

Inworld CLI

SDK Reference

Resources

Commands

Project Initialization

Working with Graphs

Local Testing

Visualization

Local Server

Cloud Deployment

A/B Testing & Experimentation

TTS

Telemetry

Next Steps

Quickstart

Deployment Guide

Need Help?

​Commands

​Project Initialization

​Working with Graphs

​Local Testing

​Visualization

​Local Server

​Cloud Deployment

​A/B Testing & Experimentation

​TTS

​Telemetry

​Next Steps

Quickstart

Deployment Guide

​Need Help?

Commands

Project Initialization

Working with Graphs

Local Testing

Visualization

Local Server

Cloud Deployment

A/B Testing & Experimentation

TTS

Telemetry

Next Steps

Need Help?