Practical overview
When it fits
Use it when you want a configurable language-model workspace for research-paper tasks, code analysis, or comparisons across multiple model responses.
Who it is for
- Researchers and students seeking language-model assistance with paper reading, translation, summaries or editing.
- Developers who want model-assisted code explanation or project-tree analysis.
- Teams or individual users interested in running and customizing a language-model interface themselves.
Documented uses
- Translate or summarize PDF and LaTeX papers.
- Polish LaTeX writing or check grammar and spelling.
- Explain code and analyze project trees.
- Query multiple language models in parallel.
- Create custom prompt shortcuts or Python function plugins.
Requirements & limits
The project provides direct Python installation instructions for Windows, Linux and macOS, as well as Docker deployment. The repository is licensed under GPL-3.0.
- Direct installation uses Python; the README recommends versions 3.9–3.11 and installing the specified requirements.
- Model access is configured through API keys or other settings; the project documents multiple model integrations.
- Some local backends, including ChatGLM and MOSS, require extra dependencies and Python and PyTorch familiarity.
- Creating function plugins requires some Python knowledge.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Gpt Academic with Python
Prepare a direct Python installation and configure model access.
Before you start
- Python; the README recommends versions 3.9–3.11.
- A model API key or other supported configuration.
- The project repository and its requirements.txt file.
Steps
Download the project repository and enter its directory.
Set API_KEY and other required variables in config.py, or use config_private.py or environment variables.
Install the dependencies with python -m pip install -r requirements.txt.
Expected outcome
The documented Python setup and configuration are in place for proceeding with the project’s run instructions.
Guide limitations
- The guide documents setup, not starting the application or completing a model query.
- The README advises installing the versions specified in requirements.txt.
- Local ChatGLM or MOSS backends need additional dependencies.
Add a custom shortcut button
Define a reusable prompt shortcut in the project’s functional-button configuration.
Before you start
- An installed project environment.
- A text editor to modify core_functional.py.
Steps
Open core_functional.py in a text editor.
Add a shortcut entry with a name and a Prefix prompt; add a Suffix if needed.
Save the file.
Expected outcome
A shortcut definition is added for a task such as translation, code explanation or text polishing.
Guide limitations
- The README also describes adding buttons through the interface’s custom-menu settings.
- This guide covers defining the shortcut, not verifying it in a running application.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 21 October 2026
- Original website title
- binary-husky/gpt_academic · Official homepage title · retained in its source language