lats

Optimize language-agent decision making with Monte Carlo Tree Search.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/MantisWare/BizForge --skill lats-mantisware
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lats
Source: https://github.com/MantisWare/BizForge/tree/main/library/skills/ai-patterns/lats
Command: npx skills add https://github.com/MantisWare/BizForge --skill lats-mantisware

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Language Agent Tree Search (LATS) combines Monte Carlo Tree Search with language-model reasoning to enable robust planning, decision making, and action selection under uncertainty.

Core Features & Use Cases

  • Monte Carlo Tree Search guided planning for language agents to navigate multi-step tasks.
  • Self-evaluating trajectories with feedback loops to improve planning quality.
  • Suitable for tasks with multiple valid solution paths or where environment signals influence decisions.

Quick Start

Run LATS to guide a language agent through complex decision trees and generate a coherent plan and actions.

Frequently Asked Questions about lats

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

FAQPage Schema
How does Monte Carlo Tree Search improve language agent planning?▼

Monte Carlo Tree Search improves language agent planning by applying a SELECT, EXPAND, SIMULATE, REFLECT, and BACKPROPAGATE workflow with UCT-based scoring to navigate complex, multi-step decision tasks under uncertainty.

When should I use Monte Carlo planning for multi-step language agent tasks?▼

You should use Monte Carlo planning for multi-step language agent tasks when the environment provides signals and feedback, or when multiple valid solution paths exist and robust decision-making under uncertainty is required.

How do I guide a language agent through complex decision trees using LATS?▼

You guide a language agent through complex decision trees by running the LATS workflow, which uses self-evaluating trajectories and feedback loops to generate coherent plans and select optimal actions.

Does LATS support integration with task orchestration frameworks?▼

Yes, LATS supports integration with task orchestration frameworks, allowing you to embed Monte Carlo Tree Search guided planning and UCT-based scoring into broader language agent environments.

What is the difference between LATS and standard language agent decision-making?▼

LATS differs from standard language agent decision-making by combining Monte Carlo Tree Search with language-model reasoning, enabling self-evaluating trajectories and feedback loops rather than relying on single-pass action selection.

What are the limitations of using UCT-based scoring for language agent planning?▼

UCT-based scoring for language agent planning may be constrained by the quality of environment signals and feedback, requiring clear simulation outcomes to effectively evaluate multi-step trajectories and navigate complex decision trees.