---
source: https://iinventedlight.com/library/notes/ao-planning-tree-search.md
title: "Tree search for agent planning (LATS)"
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
source_license: "CC BY 4.0; restated in our own words"
attribution: "Summary by iinventedlight (AI-assisted). Sources: arXiv, [2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models, CC BY 4.0; arXiv, [2404.11584] The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey, CC BY 4.0."
author: "iinventedlight (synthesis of cited sources); text drafted with AI assistance"
date: 2026-11-04
status: published
checked_on: 2026-11-04
as_of: 2026-11-04
confidence: medium
untested: "No experiment was run; only the fetched arXiv abstract pages were read, so results are as reported by the authors."
category: agent-operations
topic_area: "agent-ops: planning"
tags: agents, planning, tree-search
sources:
  - id: S1
    title: "[2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models"
    publisher: arXiv
    url: https://arxiv.org/abs/2310.04406
    accessed: 2026-11-04
    license: "CC BY 4.0"
  - id: S2
    title: "[2404.11584] The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey"
    publisher: arXiv
    url: https://arxiv.org/abs/2404.11584
    accessed: 2026-11-04
    license: "CC BY 4.0"
---
# Tree search for agent planning (LATS)

## Short answer
Language Agent Tree Search (LATS) has the agent explore several action branches with Monte Carlo Tree Search, scoring them with model-based values and self-reflection plus environment feedback. It reached 92.7% pass@1 on HumanEval. A survey of agent architectures names planning, execution and reflection as key phases for robust agents. [S1][S2]

## Claims
- id: C1
  claim: LATS integrates Monte Carlo Tree Search into language agents.
  source: S1
  evidence: "we integrate Monte Carlo Tree Search into LATS to enable LMs as agents"
- id: C2
  claim: LATS achieved 92.7% pass@1 on HumanEval.
  source: S1
  evidence: "LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval"
- id: C3
  claim: Planning, execution and reflection are key phases for robust agents.
  source: S2
  evidence: "key phases for planning, execution, and reflection that enable robust AI agent systems"

## Sources
- [S1] [2310.04406] Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models. arXiv. https://arxiv.org/abs/2310.04406 . License: CC BY 4.0. Accessed 2026-11-04.
- [S2] [2404.11584] The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey. arXiv. https://arxiv.org/abs/2404.11584 . License: CC BY 4.0. Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).