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Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models"
| Date | Stars |
|---|---|
| 2026-07-24 | 2824 |
| 2026-07-25 | 2824 |
| 2026-07-28 | 2824 |
| 2026-07-30 | 2824 |
| 2026-07-31 | 2827 |
| 2026-08-06 | 2827 |
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# Graph of Thoughts (GoT)
<p align="center">
<img src="paper/pics/preview.svg">
</p>
This is the official implementation of [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://arxiv.org/pdf/2308.09687.pdf).
This framework gives you the ability to solve complex problems by modeling them as a Graph of Operations (GoO), which is automatically executed with a Large Language Model (LLM) as the engine.
This framework is designed to be flexible and extensible, allowing you to not only solve problems using the new GoT approach, but also to implement GoOs resembling previous approaches like CoT or ToT.
## Setup Guide
In order to use this framework, you need to have a working installation of Python 3.8 or newer.
### Installing GoT
Before running either of the following two installation methods, make sure to activate your Python environment (if any) beforehand.
If you are a user and you just want to use `graph_of_thoughts`, you can install it directly from PyPI:
```bash
pip install graph_of_thoughts
```
If you are a developer and you want to modify the code, you can install it in editable mode from source:
```bash
git clone https://github.com/spcl/graph-of-thoughts.git
cd graph-of-thoughts
pip install -e .
```
### Configuring the LLM
In order to use the framework, you need to have access to an LLM.
Please follow the instructions in the [Controller README](graph_of_thoughts/controller/README.md) to configure the LLM of your choice.
## Quick Start
The following code snippet shows how to use the framework to solve the sorting problem for a list of 32 numbers using a CoT-like approach.
Make sure you have followed the [Setup Guide](#setup-guide) before running the code.
```python
from examples.sorting.sorting_032 import SortingPrompter, SortingParser, utils
from graph_of_thoughts import controller, language_models, operations
# Problem input
to_be_sorted = "[0, 2, 6, 3, 8, 7, 1, 1, 6, 7, 7, 7, 7, 9, 3, 0, 1, 7, 9, 1, 3, 5, 1, 3, 6, 4, 5, 4, 7, 3, 5, 7]"
# Create the Graph of Operations
gop = operations.GraphOfOperations()
gop.append_operation(operations.Generate())
gop.append_operation(operations.Score(scoring_function=utils.num_errors))
gop.append_operation(operations.GroundTruth(utils.test_sorting))
# Configure the Language Model (Assumes config.json is in the current directory with OpenAI API key)
lm = language_models.ChatGPT("config.json", model_name="chatgpt")
# Create the Controller
ctrl = controller.Controller(
lm,
gop,
SortingPrompter(),
SortingParser(),
# The following dictionary is used to configure the initial thought state
{
"original": to_be_sorted,
"current": "",
"method": "cot"
}
)
# Run the Controller and generate the output graph
ctrl.run()
ctrl.output_graph("output_cot.json")
```
To run the more sophisticated GoT approach, you can use the following code snippet.
```python
from examples.sorting.sorting_032 import SortingPrompter, SortingParser, got, utils
from graph_of_thoughts import controller, language_models, operations
# Problem input
to_be_sorted = "[0, 2, 6, 3, 8, 7, 1, 1, 6, 7, 7, 7, 7, 9, 3, 0, 1, 7, 9, 1, 3, 5, 1, 3, 6, 4, 5, 4, 7, 3, 5, 7]"
# Retrieve the Graph of Operations
gop = got()
# Configure the Language Model (Assumes config.json is in the current directory with OpenAI API key)
lm = language_models.ChatGPT("config.json", model_name="chatgpt")
# Create the Controller
ctrl = controller.Controller(
lm,
gop,
SortingPrompter(),
SortingParser(),
# The following dictionary is used to configure the initial thought state
{
"original": to_be_sorted,
"current": "",
"phase": 0,
"method": "got"
}
)
# Run the Controller and generate the output graph
ctrl.run()
ctrl.output_graph("output_got.json")
```
You can compare the two results by inspecting the output graphs `output_cot.json` and `output_got.json`.
The final thought states' scores indicate the number of errors in the sorted list.
## DocuExcerpt of 6,390 characters
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Ales Kubicek · United Kingdom
2
Ikko Eltociear Ashimine · Japan
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:034fb2107302b888, topic:large-language-models, topic:llm
matched fp:034fb2107302b888, topic:prompt-engineering