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README
Apache-2.0

DevChat 开源智能 IDE 插件

用自然语言生成智能工作流,以知识工程完成 AI 落地的最后一公里

虽然 GitHub Copilot、Cursor、Cline 让编码环节变得越来越智能,虽然 Dify、Flowise、扣子让工作流可以“拖拉拽”地实现,但是我们每天仍然犹如涉入了一片又一片”无AI”的海,在研发多种多样的繁琐流程中,心累地扑腾。

有人说,企业驳杂的定制要求把开发工具适配变成泥潭;也有“动手党”为了大大小小的个性化忙得不亦乐乎。 每个研发团队都有自己的性格,都值得让 AI 贴身服务,而落地这些不必很辛苦。

我们打造 DevChat 开源社区,帮助每一位开发者轻松迈过 AI 落地研发的最后一公里!

核心特性

我们为 DevChat 赋予了两个核心。

❤️ 极简私人定制,几句话创建专属工作流

  • 告别“拖拉拽”手撸工作流的死板方式和学习成本,只用几句话描述就能轻松生成智能工作流,辅助或代劳程序员完成各类任务——不论是提交一个内容规范的 GitLab MR,还是生成可执行的 API 自动测试用例;抑或是你想让 AI 边干活边语音通报进展这样的细节。
  • 开源开放社区共建,逐步积累丰富的智能工作流库,从获得丰富 IDE 上下文的插件,到各种意想不到的自主智能体,总有一款“仙家法器”适合你。
  • 多层次定制能力,既支持企业级统一要求(如代码规范),也能适配团队或个人的工具、流程和习惯。基于目录和 Git 等现有基础设施实现,秉承极简设计,不引入冗余的管理系统。

❤️ 最懂私域知识,以知识工程理解软件研发

  • 集成知识图谱能力,支持多样语义查询,查询前静态构建与查询时动态构建相结合,兼顾最佳效果与性能表现。
  • 针对具体场景分类知识,增强 AI 生成效果。
    • 例如通过分析 API 文档中所有接口、参数、功能间的各种关系,让 AI 自主测试能够组合多个 API 生成用例,减少探索步骤,提升最终测试脚本的准确性。

设计选择

从第一性原理出发,DevChat 是你长期的最佳选择

  • 仅让智能体自由发挥是不够的。这不是基础大模型能力的问题,而是个人和组织经验如何传递的问题。长期来看,我们可以假设大模型的智能达到人类水平,而人类又是如何达到高效的生产力呢?个体需要逐步积累经验,组织需要逐步形成流程。只有将这些隐性知识与 AI 结合,才可能实现研发效能的提升。工作流正是隐性知识显性化的途径。我们相信,工作流的积累是 AI 生产力工具沉淀价值的主要形式
  • 智能体仅接入工具是不够的。我们将智能体视为可自主规划和行动的工作流。虽然接入各种所需工具是智能体行动的必要条件,但更重要的是为智能体提供完成任务所需的高质量私域知识。与把重心放在工具生态的 IDE 不同,我们致力于打造先进的知识工程能力,让 DevChat 成为最懂你的私人助理。

🤝 诚挚邀请有共同技术愿景的开发者伙伴们加入我们的社区:GitHub & Discord

其他功能

  • IDE 插件基础功能:
    • 代码生成和自动补全;
    • 辅助代码理解与编辑;
    • 在项目上下文中进行高效 AI 问答。
  • 支持全球最新大模型,一次充值用所有:
    • GPT-4o/o1;
    • Claude 3.5/3.7 Sonnet;
    • DeepSeek-V3/R1;
    • Llama 3.3: 70B;
    • Qwen2.5-Turbo……
  • 集成自主接口测试:
    • 上传 API 文档,一键获得可执行的用例和脚本;
    • 全程 AI 自主完成,极少人工干预;
    • 支持多接口联动、数据校验,多角度构造复杂场景用例。
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