Home Projects dreamGPT
dreamGPT
Python

dreamGPT

Leverage hallucinations from Large Language Models (LLMs) for novelty-driven explorations.

by DivergentAI · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
agentsartificial-intelligenceautomationMITPython
View on GitHub
In plain words

Generate innovative ideas by exploring unexpected outputs from language models.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
dreamGPT — GitHub preview card
📈 Star history
590589
2026-07-202026-08-31
📈 Track dreamGPT

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Leverage hallucinations from Large Language Models (LLMs) for novelty-driven explorations.

dreamGPT has 589 stars on GitHub. It has been forked 39 times. dreamGPT is written mainly in Python. It has been in active development since 2023. dreamGPT is available under the MIT license. Its main topics are agents, artificial-intelligence, automation, generative-model.

Frequently asked questions

What is dreamGPT?

Leverage hallucinations from Large Language Models (LLMs) for novelty-driven explorations.

Is dreamGPT open source?

dreamGPT is an open-source project. It is released under the MIT license.

Is dreamGPT free?

Yes. dreamGPT is free and open source — you can use, modify and self-host it.

What license does dreamGPT use?

dreamGPT is available under the MIT license.

What language is dreamGPT written in?

dreamGPT is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — dreamGPT

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=divergentai-dreamgpt)](https://olud.ai/project/divergentai-dreamgpt.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.