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Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
| Date | Stars |
|---|---|
| 2026-07-24 | 4591 |
| 2026-07-25 | 4591 |
| 2026-07-28 | 4591 |
| 2026-07-30 | 4591 |
| 2026-07-31 | 4591 |
| 2026-08-06 | 4591 |
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[Paper link](https://arxiv.org/pdf/2305.10601.pdf)
[Author's implementation](https://github.com/princeton-nlp/tree-of-thought-llm)
## Introduction
Tree of Thoughts (ToT) is a powerful and flexible algorithm that significantly advances model reasoning by up to 70%. This plug-and-play version allows you to connect your own models and experience superintelligence!
## Install
```bash
$ pip3 install -U tree-of-thoughts
```
## Requirements
In your .env file, you need to have the following variables:
```bash
WORKSPACE_DIR="artifacts"
OPENAI_API_KEY="your_openai_api_key"
```
## Example
```python
from tree_of_thoughts import TotAgent, ToTDFSAgent
from dotenv import load_dotenv
load_dotenv()
# Create an instance of the TotAgent class
tot_agent = TotAgent(use_openai_caller=False) # Use openai caller
# Create an instance of the ToTDFSAgent class with specified parameters
dfs_agent = ToTDFSAgent(
agent=tot_agent, # Use the TotAgent instance as the agent for the DFS algorithm
threshold=0.8, # Set the threshold for evaluating the quality of thoughts
max_loops=1, # Set the maximum number of loops for the DFS algorithm
prune_threshold=0.5, # Branches with evaluation < 0.5 will be pruned
number_of_agents=4, # Set the number of agents to be used in the DFS algorithm
)
# Define the initial state for the DFS algorithm
initial_state = """
Your task: is to use 4 numbers and basic arithmetic operations (+-*/) to obtain 24 in 1 equation, return only the math
"""
# Run the DFS algorithm to solve the problem and obtain the final thought
final_thought = dfs_agent.run(initial_state)
# Print the final thought in JSON format for easy reading
print(final_thought)
"""
# Run the DFS algorithm to solve the problem and obtain the final thought
final_thought = dfs_agent.run(initial_state)
# Print the final thought in JSON format for easy reading
printExcerpt of 7,753 characters
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