PyAutoFit: Scientific Inference with Natural Language
PyAutoFit is a Python package for scientific model fitting and Bayesian inference. Bring your models, data and likelihood code, then fit models and explore results through natural language with an AI assistant or by writing Python yourself.
Getting Started
Explore with AI
The PyAutoFit AI Assistant helps you compose models, choose priors, run inference and interpret results through conversation. To get started, paste this prompt into your AI coding agent:
I want to perform scientific inference with PyAutoFit
(https://github.com/PyAutoLabs/PyAutoFit) and the
autofit_assistant (https://github.com/PyAutoLabs/autofit_assistant).
First clone the assistant repository, cd into it and follow its AGENTS.md.
Begin the "start here" guide for a new user.
See AI Options for tools and setup guidance.
Explore It Yourself
- Try it in your browser — run the introductory notebook without installing anything.
- Install PyAutoFit — get set up on your computer.
- Find examples — explore model fitting, sampling methods and plotting in the workspace.
Bring Your Own Likelihood
Already have a likelihood function for your science problem? The assistant can help connect your existing code to PyAutoFit:
Set up PyAutoFit with my existing science project:
[GitHub link or local directory].
Explain my likelihood function, propose a model and priors,
and recommend a search method.
Do not begin inference until we have discussed the setup
and I give you the go-ahead.
Then show me how to inspect and interpret the results.
The Bring Your Own Likelihood guide explains the workflow.
Learn Bayesian Inference
HowToFit teaches Bayesian inference and model fitting through worked notebooks, starting from undergraduate level. You can also read the Markdown lectures directly on GitHub.
The PyAutoFit documentation covers the software’s features, from model composition and sampling to model comparison and hierarchical inference.
Community and Contributing
Ask questions, suggest improvements and share your work in PyAutoLabs Discussions, or chat with us on Slack.
To contribute or explore community projects, visit the PyAutoFit community page.
Citations
If you use PyAutoFit in your research, see the citation guide.
License
See the license.
Release files for autofit 2026.11.12.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| autofit-2026.11.12.1.tar.gz | 8.7 MB | Without provenance | Details for autofit-2026.11.12.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| autofit-2026.11.12.1-py3-none-any.whl | Python 3 | none | any | Details for autofit-2026.11.12.1-py3-none-any.whl |
Total release size: 9.7 MB
Release files / autofit-2026.11.12.1.tar.gz
| Download URL | autofit-2026.11.12.1.tar.gz |
|---|---|
| Size | 8.7 MB |
| Tags | Source |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.16
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Release files / autofit-2026.11.12.1-py3-none-any.whl
| Download URL | autofit-2026.11.12.1-py3-none-any.whl |
|---|---|
| Size | 1.0 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.16
|
Security for autofit 2026.11.12.1
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