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tree-of-thought-llm

princeton-nlp/tree-of-thought-llm

[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

GitHub Website

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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6.0k stars624 forksLast push Jan 16, 2025 Python MIT

Decision brief

The 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure.

Good fit when

- Use 'tree-of-thought-llm' when you need an approach that handles deliberative reasoning problems, like the game of 24, leveraging large language models.
- Prefer this framework if your application requires evaluating and selecting among multiple potential next steps in a problem-solving scenario.

Avoid when

- Avoid using 'tree-of-thought-llm' for problems that do not benefit from tree-like exploration or where the solution does not involve deliberate reasoning or step-by-step evaluation.
- If real-time decision-making is critical and computational resources are limited, this tool might be too slow due to its reliance on large language models like GPT-4 which may introduce latency.

Requirements:: Min 4 GB RAM

Observed Jul 12, 2026 · Source: enrich:decision_facts

tree-of-thought-llm alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Dormant (577d since push); As of Aug 17, 2026
Provenance: Not a fork · Organization account; As of Aug 17, 2026
Security (OSV): 90 low (90 low); As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install tree-of-thought-llm

PyPI

How it fits your stack(7)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Integrates

prompts.chat

tree-of-thought-llm integrates with prompts.chat because tree-of-thought-llm uses large language models to solve problems through a tree-search strategy, which can be enhanced by the diverse and community-curated prompt collections available in prompts.chat. The integration allows users of tree-of-thought-llm to leverage a wide array of prompts from prompts.chat to optimize their problem-solving流程

integrates

open-webui

The tool "tree-of-thought-llm" implements a method for problem-solving using large language models (LLMs) through tree-search strategies, as described in the NeurIPS 2023 paper. "open-webui", being an extensible AI platform that supports various LLM runners and inference engines, can integrate with "tree-of-thought-llm" to provide a user-friendly interface for offline problem-solving tasks using L

integrates

awesome-llm-apps

Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.

MetaGPT

The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

GenAI_Agents

Both are involved in the development and implementation of GenAI agents, with tree-of-thought-llm focusing on problem-solving techniques while genai-agents provides a comprehensive repository for AI agent development.

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Similar tools

Same-category neighbours not already linked as typed edges.

ai-engineering-hub

Tutorials on LLMs, RAGs, and real-world AI agent applications

37k★

promptfoo

Test prompts, agents, and RAGs. Compare performance of various LLMs.

25k★

awesome-ai-apps

A curated list of AI applications showcasing RAG, agents, and workflows.

16k★

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A repository that implements the 'Tree of Thoughts' approach to deliberate problem-solving using large language models, demonstrated with examples like solving the game of 24.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Aug 17, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 17, 2026)

```python import argparse

Source link

README

Quick Start The following minimal script will attempt to solve the game of 24 with (might be a bit slow as it's using GPT 4): And the output would be something like (note it's not deterministic, and sometimes the output can be wrong):

For agents

This page has a .md twin and JSON over the API.

/tools/princeton-nlp-tree-of-thought-llm.md GET /api/graphcanon/tools/princeton-nlp-tree-of-thought-llm GET /api/graphcanon/graph?tool=princeton-nlp-tree-of-thought-llm