lettactl

Manages Letta AI fleets declaratively via kubectl-style CLI and YAML configurations.

45|4|Updated Dec 1, 2025

Search Tags:#deployment #cli #ai agents #declarative configuration #fleet management #lettactl

Ready to run

npx skills add https://github.com/nouamanecodes/lettactl --skill lettactl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lettactl
Source: https://github.com/nouamanecodes/lettactl/tree/main/.skills
Command: npx skills add https://github.com/nouamanecodes/lettactl --skill lettactl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the management of Letta AI agent fleets by providing a command-line interface for declarative configuration and deployment, akin to kubectl for Kubernetes.

Core Features & Use Cases

Declarative Fleet Management: Define your entire agent setup, including agents, memory blocks, tools, and files, in YAML configuration files.
Simplified Deployment: Apply your fleet configuration with a single command, ensuring consistency and reproducibility.
Use Case: You need to deploy and manage a fleet of 50 AI agents for customer support. You can define all their configurations, system prompts, and tools in a fleet.yml file and deploy them all at once using lettactl apply -f fleet.yml.

Quick Start

Use the lettactl skill to deploy your agents by applying the configuration in the file named 'fleet.yaml'.

Frequently Asked Questions about lettactl

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

FAQPage Schema

How do I manage Letta AI agent fleets using declarative configuration?▼

lettactl is a kubectl-style CLI and SDK for managing Letta AI agent fleets through declarative YAML configurations. It enables deployment, updates, and management of agents, memory blocks, tools, and files using a single apply command.

How do I deploy multiple Letta AI agents at once?▼

To deploy multiple Letta AI agents at once, define all agent configurations, system prompts, and tools in a fleet YAML file, then apply it using a single command. This ensures consistency and reproducibility across the entire fleet deployment.

Can I update memory blocks and tools for individual Letta agents?▼

Yes, you can update memory blocks and tools for individual Letta agents. The CLI supports both fleet-wide operations and individual agent management, allowing targeted resource orchestration alongside bulk configuration updates.

Does Letta fleet management work without Kubernetes?▼

Yes, Letta fleet management works without Kubernetes. It provides a standalone kubectl-style CLI that applies declarative YAML configurations directly to your Letta agents, independent of a Kubernetes cluster.

What is the best way to orchestrate resources for a large Letta AI agent deployment?▼

The best way to orchestrate resources for a large Letta AI deployment is using declarative YAML configurations. This method handles fleet-wide operations for agents, memory blocks, and tools, ensuring consistent setup across dozens of instances.

What are the limitations of using YAML configurations for Letta agent management?▼

Using YAML configurations for Letta agent management requires maintaining declarative files for all resources. While it ensures reproducibility, complex individual agent states may require careful YAML structuring to avoid fleet-wide synchronization issues.