use-llamactl-a-cli-tool-for-llamaagents

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Use llamactl to initialize, locally preview, deploy and manage LlamaIndex workflows as LlamaAgents. Required llama-index-workflows and llamactl to be installed in the environment.

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[Tool Routing and Orchestration]

When & Why to Use This Skill

This Claude skill integrates the `llamactl` CLI tool to streamline the development, testing, and deployment of LlamaIndex workflows as LlamaAgents. It provides a robust framework for developers to transition from local prototyping to production-grade cloud deployments, offering comprehensive commands for project initialization, local environment serving, and LlamaCloud management. By automating the orchestration of AI agents, it enhances developer productivity and ensures scalable AI application lifecycles.

Use Cases

  • Rapid Project Bootstrapping: Use the 'init' command to quickly create new LlamaAgents projects with standardized templates, including Python modules and optional UI frontends.
  • Local Development & Real-time Preview: Run local servers to test LlamaIndex workflows, allowing for immediate feedback on logic changes and UI adjustments before going live.
  • Seamless Cloud Deployment: Transition code from Git repositories to LlamaCloud with automated deployment configurations, including private repo support and secret management.
  • Agent Lifecycle Management: Efficiently monitor deployment statuses, update environment variables, and roll out new versions of AI agents directly through the CLI interface.
nameUse llamactl - a CLI tool for LlamaAgents
descriptionUse llamactl to initialize, locally preview, deploy and manage LlamaIndex workflows as LlamaAgents. Required llama-index-workflows and llamactl to be installed in the environment.

Use llamactl - a CLI tool for LlamaAgents

llamactl is a CLI tool for developing and deploying LlamaIndex workflows as LlamaAgents. It provides commands to initialize projects, run local development servers, and manage cloud deployments.

Prerequisites

Before using llamactl, ensure you have:

  • uv - Python package manager and build tool
  • Node.js - Required for UI development (supports npm, pnpm, or yarn)
  • llama-index-workflows and llamactl installed in your environment

Installation

Install llamactl globally using uv:

uv tool install -U llamactl
llamactl --help

Or try it without installing:

uvx llamactl --help

Initialize a Project

Create a new LlamaAgents project with starter templates:

llamactl init

This creates a Python module with LlamaIndex workflows and an optional UI frontend. Configuration is managed in pyproject.toml, where you define workflow instances, environment settings, and UI build options.

Local Development

Start the local development server:

llamactl serve

This command:

  1. Installs dependencies
  2. Serves workflows as an API (configured in pyproject.toml)
  3. Starts the frontend development server

The server automatically detects file changes and can resume in-progress workflows.

Deploy to LlamaCloud

Push your code to a git repository:

git remote add origin https://github.com/org/repo
git add -A
git commit -m 'Set up new app'
git push -u origin main

Create a cloud deployment:

llamactl deployments create

This opens an interactive Terminal UI to configure:

  • Deployment name
  • Git repository (supports private GitHub repos via the LlamaDeploy GitHub app)
  • Git branch/tag/commit
  • Environment secrets

Manage Deployments

  • View deployment status: llamactl deployments get
  • Update secrets or branch: llamactl deployments edit
  • Deploy new version: llamactl deployments update

For detailed configuration options, see the Deployment Config Reference.