登录 注册

openai-python

去点 Star
开发者工具
32,121
Stars
0
Forks
0
Watchers
0
Open Issues
未知
许可证
Python
语言
创建时间
最近更新

项目能做什么

OpenAI API 的官方 Python 客户端库。
来源:基于仓库元数据 · 内容以仓库实际 README 为准

使用说明 查看原始 README 原仓库文档

中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
# OpenAI Python API library

<!-- prettier-ignore -->
[![PyPI version](https://img.shields.io/pypi/v/openai.svg?label=pypi%20(stable))](https://pypi.org/project/openai/)

The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.10+
application. The library includes type definitions for all request params and response fields,
and offers both synchronous and asynchronous clients powered by [HTTPX2](https://httpx2.pydantic.dev/).

It is generated from our [OpenAPI specification](https://github.com/openai/openai-openapi).

## Documentation

The REST API documentation can be found on [platform.openai.com](https://platform.openai.com/docs/api-reference). The full API of this library can be found in [api.md](api.md).

## Installation

```sh
# install from PyPI
pip install openai
```

## Usage

The full API of this library can be found in [api.md](api.md).

The primary API for interacting with OpenAI models is the [Responses API](https://developers.openai.com/api/reference/resources/responses). You can generate text from the model with the code below.

```python
import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)
```

The previous standard (supported indefinitely) for generating text is the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). You can use that API to generate text from the model with the code below.

```python
from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.5",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {
            "role": "user",
            "content": "How do I check if a Python object is an instance of a class?",
        },
    ],
)

print(completion.choices[0].message.content)
```

While you can provide an `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `OPENAI_API_KEY="My API Key"` to your `.env` file
so that your API key is not stored in source control.
[Get an API key here](https://platform.openai.com/settings/organization/api-keys).

### Workload Identity Authentication

For secure, automated environments like cloud-managed Kubernetes, Azure, and Google Cloud Platform, you can use workload identity authentication with short-lived tokens from cloud identity providers instead of long-lived API keys.

#### Kubernetes (service account tokens)

```python
from openai import OpenAI
from openai.auth import k8s_service_account_token_provider

client = OpenAI(
    workload_identity={
        "identity_provider_id": "idp-123",
        "service_account_id": "sa-456",
        "provider": k8s_
阅读完整原文

商业机会分析 规则生成(AI 未配置)

综合评分 54.3 分。该项目在“个人可实施性”维度得 60 分,“付费可能性”维度得 44.0 分,差异化空间 70.0 分(评分依据见评分卡)。

将 openai-python 改造为垂直场景 SaaS

产品构想保留核心能力,针对某一类用户(如某行业团队)做托管版,收取月费。
目标用户有该需求的中小团队 / 独立站长
难度/周期中 · 2~4 周(MVP)
收费模式订阅制(如 ¥29~99/月)
竞争风险需验证付费意愿;注意许可证合规
依据 数据支持的判断:许可证与关键词显示存在商用与付费场景

面向细分需求的二次开发产品

产品构想基于 openai-python 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。
目标用户该细分场景用户
难度/周期中 · 2~6 周
收费模式买断制 / 订阅制 / 广告
竞争风险细分需求规模需验证
依据 AI 推测:基于项目主题标签的判断

封装为在线小工具或 API 服务

产品构想把项目最常用的一个功能做成网页小工具,靠流量变现或按量计费。
目标用户搜索引擎带来的长尾用户
难度/周期低 · 1~3 周
收费模式免费引流 + 增值功能 / API 按量计费
竞争风险同质化竞争,需做好 SEO
依据 AI 推测:基于技术栈与部署特征的判断
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心

竞品与相似项目 GitHub Search 真实检索

项目中文简介Stars语言
BerriAI/litellm 中文介绍暂未生成
The fastest, litest AI Gateway. Rust core with Python SDK. C
63,075 Python
openai/openai-agents-python 中文介绍暂未生成
A lightweight, powerful framework for multi-agent workflows
30,603 Python
dennybritz/reinforcement-learning 中文介绍暂未生成
Implementation of Reinforcement Learning Algorithms. Python,
22,177 Jupyter Notebook
Michael-A-Kuykendall/shimmy 中文介绍暂未生成
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible,
6,015 Rust
AgentOps-AI/agentops 中文介绍暂未生成
Python SDK for AI agent monitoring, LLM cost tracking, bench
5,954 Python
father-bot/chatgpt_telegram_bot 中文介绍暂未生成
💬 Telegram bot with ChatGPT & Claude Python-based, using Ope
5,557 Python
以上为通过 GitHub 官方 Search API 检索的相似项目,功能差异与优劣势需进一步人工确认,配置 AI 后可生成对比分析。

项目优势与风险

技术成熟度创建于 ,最近推送 ,发布过 100 个 Release
社区活跃Star 32,121 / Fork 0 / Issue 0(数据支持,可自行核实)
许可证未检测到(需人工确认)
主要风险上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价

安装与使用

快速开始(以仓库 README 为准)
git clone https://github.com/openai/openai-python.git
cd openai-python
# 请阅读仓库 README 获取确切安装步骤(依赖、配置、启动命令)
阅读原仓库文档
术语说明:Docker=应用容器(把程序和依赖打包运行);API Key=调用外部服务的密钥;Git Clone=把仓库代码下载到本地。常见错误:API Key 未配置 / 端口占用 / 数据库连接失败,多数项目在 README 的 Troubleshooting 章节有说明。