// 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.
Invoke this skill BEFORE implementing any text extraction/parsing logic to learn how to use LlamaParse to process any document accurately. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
Invoke this skill BEFORE implementing any structured data extraction from documents to learn the correct llama_cloud_services API usage. Required reading before writing extraction code. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
Invoke this skill BEFORE implementing any text/document classification task to learn the correct llama_cloud_services API usage. Required reading before writing classification code." Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
Leverage Retrieval Augmented Generation to retrieve relevant information from a a LlamaCloud Index. Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
| name | Use llamactl - a CLI tool for LlamaAgents |
| description | 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. |
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.
Before using llamactl, ensure you have:
uv - Python package manager and build toolnpm, pnpm, or yarn)llama-index-workflows and llamactl installed in your environmentInstall llamactl globally using uv:
uv tool install -U llamactl
llamactl --help
Or try it without installing:
uvx llamactl --help
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.
Start the local development server:
llamactl serve
This command:
pyproject.toml)The server automatically detects file changes and can resume in-progress workflows.
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:
llamactl deployments getllamactl deployments editllamactl deployments updateFor detailed configuration options, see the Deployment Config Reference.