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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
| Date | Stars |
|---|---|
| 2026-07-24 | 3044 |
| 2026-07-25 | 3045 |
| 2026-07-28 | 3045 |
| 2026-07-30 | 3045 |
| 2026-07-31 | 3045 |
| 2026-08-06 | 3045 |
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<h1 align="center">
<a href="https://hegel-ai.com">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="img/hegel_ai_logo_dark.svg">
<img height="70" src="img/hegel_ai_logo.svg">
</picture>
</a>
<br>
PromptTools
</h1>
<p align="center">
:wrench: Test and experiment with prompts, LLMs, and vector databases. :hammer:
<p align="center">
<a href="http://prompttools.readthedocs.io/"><img src="https://img.shields.io/badge/View%20Documentation-Docs-yellow"></a>
<a href="https://discord.gg/7KeRPNHGdJ"><img src="https://img.shields.io/badge/Join%20our%20community-Discord-blue"></a>
<a href="https://pepy.tech/project/prompttools" target="_blank"><img src="https://static.pepy.tech/badge/prompttools" alt="Total Downloads"/></a>
<a href="https://github.com/hegelai/prompttools"><img src="https://img.shields.io/github/stars/hegelai/prompttools" /></a>
<a href="https://twitter.com/hegel_ai"><img src="https://img.shields.io/twitter/follow/Hegel_AI?style=social"></a>
</p>
Welcome to `prompttools` created by [Hegel AI](https://hegel-ai.com/)! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like _code_, _notebooks_, and a local _playground_.
In just a few lines of code, you can test your prompts and parameters across different models (whether you are using
OpenAI, Anthropic, or LLaMA models). You can even evaluate the retrieval accuracy of vector databases.
```python
from prompttools.experiment import OpenAIChatExperiment
messages = [
[{"role": "user", "content": "Tell me a joke."},],
[{"role": "user", "content": "Is 17077 a prime number?"},],
]
models = ["gpt-3.5-turbo", "gpt-4"]
temperatures = [0.0]
openai_experiment = OpenAIChatExperiment(models, messages, temperature=temperatures)
openai_experiment.run()
openai_experiment.visualize()
```
To stay in touch with us about issues and future updates, join the [Discord](https://discord.gg/7KeRPNHGdJ).
## Quickstart
To install `prompttools`, you can use `pip`:
```
pip install prompttools
```
You can run a simple example of a `prompttools` locally with the following
```
git clone https://github.com/hegelai/prompttools.git
cd prompttools && jupyter notebook examples/notebooks/OpenAIChatExperiment.ipynb
```
You can also run the notebook in [Google Colab](https://colab.research.google.com/drive/1YVcpBew8EqbhXFN8P5NaFrOIqc1FKWeS?usp=sharing)
## Playground
<p align="center">
<img src="img/playground.gif" width="1000" height="500">
</p>
If you want to interact with `prompttools` using our playground interface, you can launch it with the following commands.
You can run a simple example of a `prompttools` locally with the following
```
pip install notebook # If jupyter notebook has not been installed
pip install prompttools
```
Then, clone the git repo and launch the streamlit app:
```
git clone https://github.com/hegelai/prompttools.git
cd prompttools && streamlit run prompttools/playground/playground.py
```
You can also access a hosted version of the playground on the [Streamlit Community Cloud](https://prompttools.streamlit.app/).
> Note: The hosted version does not support LlamaCpp
## Documentation
Our [documentation website](https://prompttools.readthedocs.io/en/latest/index.html) contains the full API reference
and more description of individual components. Check it out!
## Supported Integrations
Here is a list of APIs that we support with our experiments:
LLMs
- OpenAI (Completion, ChatCompletion, Fine-tuned models) - **Supported**
- LLaMA.Cpp (LLaMA 1, LLaMA 2) - **Supported**
- HuggingFace (Hub API, Inference Endpoints) - **Supported**
- Anthropic - **Supported**
- Mistral AI - **Supported**
- Google Gemini - **Supported**
- Google PaLM (legacy) - **Supported**
- Google Vertex AI - **Supported**
- Azure OpenAI Service - **Supported**
- RepExcerpt of 6,917 characters
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Steven Krawczyk
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Ayush Chaurasia · India
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Maurício Maia · Brazil
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λx . ophidia ereц grex⋆. 𐙚 ̊ . أوفيديا إريكس جريكس . · #エメラダ bc1qgfwzmkkqj09kf88fha6ztd597434xhz3zv487t
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Tony Kipkemboi · Rekody · United States
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Isaac Malsky
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Kacper Łukawski · Poland
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:73edf74d06ee4f36, topic:vector-search
matched fp:73edf74d06ee4f36, topic:deep-learning
matched fp:73edf74d06ee4f36, topic:large-language-models
matched fp:73edf74d06ee4f36, topic:embeddings