Best 98 AI Developer Tools AI Tools & Websites in 2025
PDF.co, CybeDefend, CSS Picker, ScoutDB, CometAPI, Bugster, Basin MCP, BLACKBOX.AI, Replit, Cursor
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PDF.co
用于提取、编辑、转换和管理 PDF 文档的 API。
C
CybeDefend
用于实时漏洞检测和修复的AI应用安全解决方案。
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CSS Picker
用于轻松将设计转换为代码的 AI 工具。
ScoutDB
全球首款代理 Mongo GUI。数据导航速度提升 90%。精美
CometAPI
CometAPI 是一站式大模型 API 聚合平台，提供便捷高效的 API 服务集成与管理，是一套完整的贯穿 API 整个生命周期的工具，帮助研发团队践行 API 设计优先开发的最佳实践，让 AI 开发更简单。
Bugster
为开发人员提供 AI 测试解决方案，以实现测试自动化并提高效率。
Basin MCP
可靠性工具可阻止 AI 代码生成幻觉。
BLACKBOX.AI
用于转变工作和学习体验的人工智能代理。
Replit
Replit 是一个协作式 IDE，支持 50 多种编程语言，提供强大的编译器和解释器。
Cursor
用于快速软件开发的 AI-first 代码编辑器。
Windsurf
人工智能代码编辑器通过主动协助提高开发人员的工作效率。
Weights & Biases
供机器学习开发人员跟踪、可视化和优化实验的平台。
gptengineer.app
使用英语构建网络应用程序，快速且轻松地进行原型设计。
mcp server
增强 AI 功能的第三方 MCP 服务器的中央目录。
Replicate AI
通过 API 执行 AI 模型。
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KaneAI
LambdaTest 的 KaneAI 是一款基于 AI 的智能测试代理，专为高速质量工程团队打造。KaneAI 基于现代大型语言模型 (LLM) 构建，允许团队使用自然语言创建、调试和改进测试。
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Augment Code
人工智能平台赋能开发人员提高编码效率和协作能力。
Composio
用于 AI 代理和 LLM 的集成平台简化了 API 连接。
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Query Search
使用自然语言寻找开发解决方案。
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Relume
G
Gemini Code Assist
为 IDE 中的开发人员提供 AI 编码帮助
C
Create
人工智能驱动的开发人员可以快速开发应用程序。
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Continue
使用 Continue（一种用于软件开发的开源自动驾驶仪）将 AI 文本生成引入您的 IDE。
CodeRabbit
CodeRabbit 是一款 AI 工具，可以利用 AI 洞察加快代码审查速度。
Hex
用于协作分析的现代数据平台。
什么是 AI Developer Tools
AI Developer Tools refer to the software applications or platforms designed specifically for supporting the work of AI development. They assist programmers in creating, testing, and deploying AI models more efficiently. These tools often encompass a range of features, including but not limited to data preprocessing, model building, training, validation, and deployment.
最受欢迎的十大 AI Developer Tools 类AI工具和网站
| No. | Name | Description |
|---|---|---|
| 1 | PDF.co | 用于提取、编辑、转换和管理 PDF 文档的 API。 |
| 2 | CybeDefend | 用于实时漏洞检测和修复的AI应用安全解决方案。 |
| 3 | CSS Picker | 用于轻松将设计转换为代码的 AI 工具。 |
| 4 | ScoutDB | 全球首款代理 Mongo GUI。数据导航速度提升 90%。精美 |
| 5 | CometAPI | CometAPI 是一站式大模型 API 聚合平台，提供便捷高效的 API 服务集成与管理，是一套完整的贯穿 API 整个生命周期的工具，帮助研发团队践行 API 设计优先开发的最佳实践，让 AI 开发更简单。 |
| 6 | Bugster | 为开发人员提供 AI 测试解决方案，以实现测试自动化并提高效率。 |
| 7 | Basin MCP | 可靠性工具可阻止 AI 代码生成幻觉。 |
| 8 | BLACKBOX.AI | 用于转变工作和学习体验的人工智能代理。 |
| 9 | Replit | Replit 是一个协作式 IDE，支持 50 多种编程语言，提供强大的编译器和解释器。 |
| 10 | Cursor | 用于快速软件开发的 AI-first 代码编辑器。 |
Newest AI Developer Tools AI Tools & Websites
| No. | Name | Description |
|---|---|---|
| 1 | DeepShare | An experimental all-in-one deep content sharing community for high-quality content. |
| 2 | DevBooster | AI browser extension for smarter copy-paste coding, validation, optimization, and documentation. |
| 3 | Propolis | AI-powered autonomous browser agents for QA automation. |
AI Developer Tools Core Features
- Data preprocessing: AI Developer Tools often include features for cleaning and transforming raw data into a useful format for AI models.
- Model building: These tools usually provide a variety of built-in AI algorithms that developers can use to build their models.
- Training and validation: They typically offer functionalities for training AI models on data sets and validating their performance.
- Deployment: AI Developer Tools commonly come with capabilities for deploying trained AI models into different environments.
Who is suitable to use AI Developer Tools
AI Developer Tools are suitable for use by various groups of people. Software developers, Data Scientists, AI Researchers are the prime users. Industries or sectors like Information Technology, Manufacturing, Healthcare, Finance, and many others would find these tools beneficial.
How does AI Developer Tools work?
AI Developer Tools work in a varying way depending on their individual specifics. However, they generally involve a typical process of data ingestion, preprocessing, model design, training, testing, and finally, deployment. Developers interact with these tools using their preferred programming languages, while some advanced tools also provide a drag-and-drop interface for users with no coding proficiency.
Advantages of AI Developer Tools
AI Developer Tools confer many benefits, including: Increased productivity as they significantly reduce the time and efforts in creating AI models, Improved accuracy in model creation as they come with built-in best practices, Scalability through the support for cloud-based data storage and computing resources, and Accessibility making AI technology achievable to non-technical individuals.
