diff --git a/README.md b/README.md
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@@ -224,6 +224,7 @@ RL-trained models that run up to 8 parallel searches per turn to retrieve code c
 - **[pro-workflow](https://github.com/rohitg00/pro-workflow)**: Self-correcting memory system that lets Claude Code learn from its mistakes, covering context engineering, parallel worktrees, agent teams, and 17 skills
 - **[kit](https://github.com/cased/kit)**: Context engineering toolkit for AI dev tools, providing codebase mapping, symbol extraction, and code search
 - **[RepoPrompt CE](https://github.com/repoprompt/repoprompt-ce)**: Community edition of RepoPrompt, a native macOS context engineering app for AI coding agents, with an MCP CLI
+- **[DevProjex](https://github.com/Avazbek22/DevProjex)**: Builds clean, AI-ready project context with folder trees, file contents, token counting, Smart Ignore, preview, and multi-format export through a fast GUI and CLI.
 - **[context-engineering (outcomeops)](https://github.com/outcomeops/context-engineering)**: Working reference implementation of context engineering with five components (corpus, retrieval, injection, output, enforcement), running end-to-end on Amazon Bedrock
 - **[Project Context Records (PCR)](https://github.com/hyf0/project-context-records)**: A context-engineering methodology that keeps a durable, repo-versioned archive of a project's meta-context (the why, architecture, and maintainer decisions) so AI collaborators inherit its judgment instead of re-deriving it
 - **[interview-prep-template](https://github.com/AbhiK189/interview-prep-template)**: A three-layer context-engineering template (immutable sources → agent-maintained wiki → operating-manual file) where the agent synthesizes raw material into reusable answers, frameworks, and scored debriefs that compound over time
diff --git a/README_CN.md b/README_CN.md
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@@ -218,6 +218,7 @@ Context工程是对大语言模型(LLM)信息负载的系统性优化。它包
 - **[pro-workflow](https://github.com/rohitg00/pro-workflow)**：让 Claude Code 从纠错中学习的自我修正记忆系统，涵盖上下文工程、并行 worktree、agent 团队与 17 个 skills
 - **[kit](https://github.com/cased/kit)**：面向 AI 开发工具的上下文工程工具包，提供代码库映射、符号抽取与代码搜索
 - **[RepoPrompt CE](https://github.com/repoprompt/repoprompt-ce)**：RepoPrompt 社区版，面向 AI 编码 agent 的 macOS 原生上下文工程应用，附带 MCP CLI
+- **[DevProjex](https://github.com/Avazbek22/DevProjex)**：通过快速的 GUI 和 CLI，使用目录树、文件内容、Token 统计、Smart Ignore、预览和多格式导出，构建整洁且适用于 AI 的项目上下文。
 - **[context-engineering（outcomeops）](https://github.com/outcomeops/context-engineering)**：上下文工程的可运行参考实现，含语料、检索、注入、输出、强制执行五大组件，在 Amazon Bedrock 上端到端运行
 - **[Project Context Records (PCR)](https://github.com/hyf0/project-context-records)**：一套上下文工程方法论，在仓库内持久化、版本化地存档项目的「元上下文」（缘由、架构、维护者决策），让 AI 协作者继承项目判断力，而非反复重新推导
 - **[interview-prep-template](https://github.com/AbhiK189/interview-prep-template)**：三层上下文工程模板（不可变原始素材 → agent 维护的 wiki → 运行手册文件），由 agent 把原始素材合成为可复用的答案、框架与带评分的复盘，并随每次面试持续累积变强