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letta-fleet-management

Manage Letta AI agent fleets declaratively via YAML and a kubectl-style CLI.

Updated Feb 24, 2026
Search Tags:#multi-tenancy#kubectl#canary deployment#agent management#declarative configuration#fleet management
One-click install
npx skills add https://github.com/Zurybr/lefarma-skills --skill letta-fleet-management
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: letta-fleet-management
Source: https://github.com/Zurybr/lefarma-skills/tree/main/letta/fleet-management
Command: npx skills add https://github.com/Zurybr/lefarma-skills --skill letta-fleet-management

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the management of multiple AI agents, enabling declarative control over their configurations, deployments, and operations, akin to infrastructure-as-code.

Core Features & Use Cases

  • Declarative Fleet Definition: Define agent fleets using a fleet.yaml file, specifying shared configurations, memory blocks, tools, and individual agent settings.
  • Automated Deployment & Updates: Apply fleet configurations using a kubectl-style CLI (lettactl apply), managing creation, updates, and rollouts.
  • Advanced Management Features: Supports canary deployments, multi-tenancy via tags, bulk messaging, agent calibration, and fleet-wide reporting for memory health.
  • Use Case: A company needs to deploy and manage 50 customer support agents, each with slightly different configurations but sharing common knowledge bases and tools. This Skill allows them to define this entire fleet in a single YAML file and manage it efficiently.

Quick Start

Use the letta-fleet-management skill to apply the agent configuration defined in the file 'fleet.yaml'.

Frequently Asked Questions about letta-fleet-management

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

FAQPage Schema
How do I manage multiple AI agents declaratively?▼

You can manage multiple AI agents declaratively by defining shared configurations, memory blocks, and tools in a single fleet.yaml file and applying it to create or update the entire fleet.

What is the best way to deploy a fleet of agents with different configurations?▼

Deploying a fleet of agents with different configurations is best handled by defining shared and individual settings in a YAML file, then using a kubectl-style CLI to apply the declarative configuration.

Can I use a kubectl-style CLI for agent management?▼

Yes, agent management is supported through a kubectl-style CLI that allows you to apply configurations, manage rollouts, and perform bulk operations on your agent fleet.

Does Letta fleet management support canary deployments and multi-tenancy?▼

Letta fleet management supports canary deployments for gradual rollouts and multi-tenancy via tags, allowing you to isolate and manage distinct groups of agents within the same fleet.

How do I apply a configuration to a Letta agent fleet?▼

To apply a configuration to a Letta agent fleet, you use the lettactl apply command with your fleet.yaml file, which handles the creation, update, and rollout of agents based on the declarative configuration.

What are the limitations of declarative fleet management for AI agents?▼

The metadata does not specify explicit limitations, but declarative fleet management for AI agents is designed for shared configurations and bulk operations rather than highly individualized, ad-hoc agent modifications.

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