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gpt_academic

binary-husky/gpt_academic

提供实用化交互接口,优化论文阅读/润色/写作体验

GraphCanon updated 4d · GitHub synced 4d

71k stars8.3k forksLast push 6mo Python GPL-3.0

Decision brief

gpt_academic专为增强与GPT/GLM等大语言模型的交互,优化论文写作、润色和阅读体验。它支持自定义模块、多种LLM接入,并且拥有PDF/LaTeX文档处理功能。

Good fit when

  • 需要使用GPT或GLM大语言模型进行高效的学术论文相关任务时
  • 想要通过自定义快捷按钮和插件提高工作流效率,特别是在处理和技术性的写作活动中

Avoid when

  • 主要任务不是围绕论文和学术资料处理时, 特别是不涉及到大语言模型的实际应用情况
  • 不需要自定义快捷按钮与高级功能插件,且对通用的大语言模型交互界面已经满意
Pricing:
freemium
Requirements:
Min 8 GB RAM; 依赖Python环境

Observed Jul 11, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Slowing (203d since push)
As of 4d
Provenance
Not a fork · Personal account
As of 4d
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install gpt_academic
PyPI

How it fits your stack(29)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

Integrates

Related

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Similar tools

Same-category neighbours not already linked as typed edges.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

为GPT/GLM等大语言模型提供了实用化的交互界面,专注于提升论文相关的任务效率。支持自定义功能和多种LLM接入。

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Aug 17, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Aug 17, 2026

Languages
python

Source: github.language · Aug 17, 2026

Categories

Graph entities

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 17, 2026)

W1 --> W11["1 Python pip包管理依赖"]
Source link
Works with ChatGPTChatGPT

Source: README excerpt (regex_v1, Aug 17, 2026)

1. 仅ChatGPT + GLM4 + 文心一言+spark等在线模型(推荐大多数人选择)
Source link

Tags

README

Installation

flowchart TD
    A{"安装方法"} --> W1("I 🔑直接运行 (Windows, Linux or MacOS)")
    W1 --> W11["1 Python pip包管理依赖"]
    W1 --> W12["2 Anaconda包管理依赖(推荐⭐)"]

    A --> W2["II 🐳使用Docker (Windows, Linux or MacOS)"]

    W2 --> k1["1 部署项目全部能力的大镜像(推荐⭐)"]
    W2 --> k2["2 仅在线模型(GPT, GLM4等)镜像"]
    W2 --> k3["3 在线模型 + Latex的大镜像"]

    A --> W4["IV 🚀其他部署方法"]
    W4 --> C1["1 Windows/MacOS 一键安装运行脚本(推荐⭐)"]
    W4 --> C2["2 Huggingface, Sealos远程部署"]
    W4 --> C4["3 其他 ..."]

【可选步骤V】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型,目前支持的全部模型如下(jittorllms系列目前仅支持docker方案):

AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]


安装方法II:使用Docker

  1. 部署项目的全部能力(这个是包含cuda和latex的大型镜像。但如果您网速慢、硬盘小,则不推荐该方法部署完整项目)

    # 修改docker-compose.yml,保留方案0并删除其他方案。然后运行:
    docker-compose up
    
  2. 仅ChatGPT + GLM4 + 文心一言+spark等在线模型(推荐大多数人选择)

    # 修改docker-compose.yml,保留方案1并删除其他方案。然后运行:
    docker-compose up
    

P.S. 如果需要依赖Latex的插件功能,请见Wiki。另外,您也可以直接使用方案4或者方案0获取Latex功能。

  1. ChatGPT + GLM3 + MOSS + LLAMA2 + 通义千问(需要熟悉Nvidia Docker运行时)

    # 修改docker-compose.yml,保留方案2并删除其他方案。然后运行:
    docker-compose up
    

For agents

This page has a .md twin and JSON over the API.

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