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产品介绍\n\n```text\n _ \n| |__ ___ _ __ _ __ ___ ___ ___ _ __ ___ _ __ ___\n| '_ \\ / _ \\ '__| '_ ' _ \\ / _ \\/ __| | '__/ _ \\ '_ \\ / _ \\\n| | | | __/ | | | | | | | __/\\__ \\ | | | __/ |_) | (_) |\n|_| |_|\\___|_| |_| |_| |_|\\___||___/ |_| \\___| .__/ \\___/\n |_| \n\nrepo-local memory for AI coding assistants\ncapture -> consolidate -> inject\n```\n\n\n你有没有遇到过这种情况：打开一个新的 AI 编程会话，第一件事不是写代码，而是重新解释一遍项目约定。\n\n“这个仓库用 bun。”\n\n“API 客户端在这里。”\n\n“上次那个 bug 的根因不是类型问题，是权限边界没处理好。”\n\n这些信息明明已经在某次对话里讲过，但下一次会话又消失了。`hermes-repo` 想解决的就是这个问题：**把 AI 编程助手的项目记忆放回 Git 仓库。**\n\n![AI 编程会话里的上下文丢失](https://neptune-ipc.oss-cn-shenzhen.aliyuncs.com/img/20260708173721600.png)\n\n## hermes-repo 是什么\n\n`hermes-repo` 是一个 repo-local memory 工具。它会把 Claude Code、Cursor、CodeBuddy、OpenAI Codex 等助手的 hook 接到当前仓库里，让项目上下文形成一个闭环：\n\n- 会话结束时，捕获有价值的上下文。\n- 需要整理时，用 OpenAI 兼容 LLM 把原始记录转成结构化知识。\n- 下一次会话开始时，自动把项目记忆注入给助手。\n\n一句话说：**它让 AI 助手不只记住这一轮聊天，而是记住这个仓库。**\n\n## 相比工具自带记忆，它的优势在哪里\n\n现在不少 AI 编程工具都有自己的记忆、规则或项目上下文能力，这些能力很有用，但通常会绑定在某一个产品、某一个账号或某一个编辑器环境里。`hermes-repo` 的思路不一样：它把记忆层从具体工具里抽出来，放到项目仓库本身。\n\n这带来几个直接优势：\n\n- **跨助手可用**：同一份项目记忆可以服务 Claude Code、Cursor、CodeBuddy、OpenAI Codex，而不是被锁在单一工具里。\n- **跟着 Git 仓库走**：真正重要的项目规则、工作流、架构决策可以进入 `.memory/`，随代码一起演进。\n- **可审计、可修改**：记忆不是黑盒。你可以直接打开 Markdown 文件，看它记录了什么，也可以手动修正。\n- **区分本地隐私和团队知识**：密钥、原始 transcript、处理状态留在本地并默认忽略；沉淀后的结构化知识可以按需纳入版本控制。\n- **适合长期项目**：工具内置记忆更像“助手记住你”，`hermes-repo` 更像“项目记住自己”。成员换工具、换机器、换会话，核心上下文仍然留在仓库里。\n\n所以它不是要替代各个工具的内置记忆，而是补上更底层的一层：**让项目拥有一份独立、透明、可迁移的长期记忆。**\n\n![hermes-repo 的记忆闭环](https://neptune-ipc.oss-cn-shenzhen.aliyuncs.com/img/20260708173801133.png)\n\n## 它解决的不是“聊天记录保存”，而是“项目知识沉淀”\n\n普通聊天记录的问题是：信息存在，但不可用。你要自己翻、自己总结、自己复制给下一轮会话。\n\n`hermes-repo` 的设计更接近一个项目知识库：\n\n- `.memory/MEMORY.md`：下一次会话注入用的导航摘要。\n- `.memory/rules/`：每次注入都会全文加载的规则。\n- `.memory/domains/`：领域知识和业务背景。\n- `.memory/workflows/`：可复用开发流程。\n- `.memory/decisions/`：架构和产品决策。\n- `.memory/incidents/`：踩坑记录和根因分析。\n\n这些结构化知识默认可以跟着 Git 仓库走；而 `.memory/config.json`、原始 transcript、处理状态等本地敏感信息会被 gitignore。\n\n## 为什么这件事重要\n\nAI 编程的效率瓶颈，很多时候不在模型会不会写代码，而在它是否理解当前项目：\n\n- 它知道你的包管理器、目录结构和命名风格吗？\n- 它知道哪些方案之前试过、为什么放弃吗？\n- 它知道某个 bug 的真实根因和修复边界吗？\n- 它知道团队约定哪些文件能动、哪些文件不能随便改吗？\n\n如果每次都靠人重新解释，AI 只是一次性外援。 \n如果上下文能被沉淀、整理、注入，它才更像项目里的长期协作者。\n\n## 从个人仓库记忆，到团队级记忆系统\n\n当前 monorepo 已经拆成三层：\n\n- `@riconext/hermes-repo`：CLI、hooks、本地 `.memory/` 工作流，已经发布 npm。\n- `@riconext/hermes-mcp-server`：基于 FastMCP + PostgreSQL 的团队记忆 MCP 服务，提供 list/add/search/promote/delete memory 等工具。\n- `@riconext/hermes-ui`：基于 Next.js 16 + Shadcn/ui 的 Web 管理界面，用来管理项目和记忆。\n\n![hermes-repo 的模块结构](https://neptune-ipc.oss-cn-shenzhen.aliyuncs.com/img/20260708173837700.png)\n\n更重要的是，MCP 服务和 UI 不依赖某个第三方托管平台。你可以把它们部署在自己的机器、内网服务器或团队基础设施里，数据库、访问权限、记忆内容、升级节奏都由自己掌控。对于不希望项目知识散落在不同 SaaS 工具里的团队，这一点尤其关键。\n\n\n这个方向很明确：先让每个仓库拥有自己的长期记忆，再把有价值的经验提升为团队共享知识。\n\n## 适合谁\n\n如果你经常在一个项目里使用 AI 编程助手，`hermes-repo` 适合你。\n\n如果你的团队已经在 Claude Code、Cursor、CodeBuddy 或 Codex 之间切换，它更适合你。\n\n如果你受够了“每次开新会话都像新人入职”，那它解决的正是这个问题。\n\n项目地址：\n\n```text\nhttps://github.com/ricoNext/hermes-repo\n```\n\n安装入口：\n\n```bash\nnpx @riconext/hermes-repo init\n```\n\n## 整体架构\n\n```text\n用户运行: npx @riconext/hermes-repo init\n |\n v\n创建/合并:\n .memory/\n config.json # 本地配置，包含 LLM key，gitignored\n MEMORY.md # 会话注入用导航摘要\n rules/ # 每次注入时全文加载\n domains/general/ # 领域知识\n workflows/ # 可复用流程\n decisions/ # 架构/产品决策\n incidents/ # 踩坑和根因记录\n captures/raw/ # 原始会话捕获，gitignored\n captures/archived/ # 已归档捕获，gitignored\n consolidate-state.json # 本地处理状态，gitignored\n AGENTS.md # 共享助手引导\n assistant hook config # .claude, .cursor, .codebuddy, .codex\n\n运行时:\n SessionStart -> hermes-repo inject\n 读取 MEMORY.md + rules/*.md\n 按助手 hook 协议输出\n\n Stop -> hermes-repo capture\n 解析当前助手 transcript\n 追加到 captures/raw/session-{id}.md\n 可选：排队后台 LLM 升级任务\n 满足阈值时可能调度后台 flush（autoFlush）\n\n 手动 -> hermes-repo flush\n 需要已配置 LLM\n 读取 pending/stale 原始会话\n 写入 rules/domains/workflows/decisions/incidents\n 重新生成 MEMORY.md\n```\n\n## 存储模型\n\n| 层级 | 路径 | Git 行为 | 用途 |\n|------|------|----------|------|\n| 本地 | `.memory/config.json`、`.memory/captures/`、`.memory/consolidate-state.json`、`.memory/.consolidate.lock` | init 写入的 gitignore 块会忽略 | 密钥、会话记录、处理状态 |\n| 知识库 | `.memory/MEMORY.md`、`.memory/rules/`、`.memory/domains/`、`.memory/workflows/`、`.memory/decisions/`、`.memory/incidents/` | 默认纳入版本控制（除非你自行 gitignore） | 注入到后续会话的结构化记忆 |\n| 助手引导 | `AGENTS.md`、已选择助手的配置文件 | 普通仓库文件 | 告诉助手如何使用记忆 |\n\n\n## 上手指南\n\n在项目 Git 根目录执行：\n\n```bash\nnpx @riconext/hermes-repo init\n```\n\n交互式 `init` 会询问：\n\n- 目标仓库目录\n- 要接入哪些助手\n- 是否写入 capture 示例模板到 `.memory/templates/`\n- 是否现在配置 OpenAI 兼容 LLM\n\n如果在 init 阶段配置 LLM，hermes-repo 会写入 `.memory/config.json`，并在结束摘要中确认 `flush` 是否可用。LLM 不完整时，`capture` 和 `inject` 仍可用，但 `flush` / `autoFlush` 暂时无法整理记忆。\n\n> `flush` / `autoFlush` 非常重要， 他是用大模型对原始对话记忆进行归纳总结，生成记忆地图索引， 并在下一次对话中将记忆地图索引注入到上下文中。\n\n其他 init 参数：\n\n | 参数 | 说明 |\n |------|------|\n | `-y, --yes` | 非交互模式，使用默认选项（跳过所有询问） |\n | `-f, --force` | 覆盖已存在的脚手架文件（不删除 captures 等内容） |\n | `-C, --cwd ` | 目标目录，默认为当前工作目录 |\n | `--tools ` | 逗号分隔的助手 id，如 `claude-code,cursor`（**必须与 `-y` 合用**） |\n | `--mcp-project-id ` | 非交互模式：启用 MCP 并绑定团队项目 UUID |\n | `--mcp-server-url ` | 非交互模式：MCP 服务地址，默认 `http://localhost:3000` |\n | `--mcp-user-id ` | 非交互模式：MCP 用户 UUID，用于推送记忆时关联用户 |\n\n使用示例：\n\n```bash\n# 交互式初始化\n hermes-repo init\n\n # 非交互模式，使用默认助手\n hermes-repo init -y\n\n # 非交互模式，指定多个助手\n hermes-repo init -y --tools claude-code,cursor\n\n # 非交互模式 + 启用 MCP\n hermes-repo init -y \\\n --mcp-project-id \"uuid-here\" \\\n --mcp-user-id \"user-uuid-here\" \\\n --mcp-server-url \"http://localhost:3000\"\n\n # 强制覆盖已有文件\n hermes-repo init -y -f\n\n # 在指定目录初始化\n hermes-repo init -y -C /path/to/repo\n```\n\n 注意：\n - --tools 参数需要与 -y 一起使用，否则会报错\n - MCP 相关参数只在非交互模式下有效\n\n\n之后正常使用助手：\n\n1. 会话开始时，hook 运行 `inject` 注入 `MEMORY.md` 导航摘要。\n2. 会话结束时，hook 运行 `capture` 进行原始会话捕获。\n3. 积累了原始捕获且已配置 LLM 后，可等待 `autoFlush` 自动整理，或手动执行：\n\n```bash\nnpx @riconext/hermes-repo flush\n```\n\n## LLM 配置\n\n配置 LLM 后才能启用整理能力。`flush`、`capture-llm`、`autoFlush` 都依赖 LLM。\n\nhermes-repo 使用 OpenAI 兼容的 Chat Completions 接口：\n\n```json\n{\n \"llm\": {\n \"enabled\": true,\n \"provider\": \"openai\",\n \"baseUrl\": \"https://api.deepseek.com\",\n \"model\": \"deepseek-v4-flash\",\n \"apiKey\": \"你的密钥\",\n \"timeoutMs\": 60000,\n \"maxInputChars\": 24000,\n \"mode\": \"async\"\n },\n \"consolidate\": {\n \"autoFlush\": {\n \"enabled\": true,\n \"minPendingSessions\": 3,\n \"minIntervalMinutes\": 30,\n \"maxPendingChars\": 20000\n }\n }\n}\n```\n\n注意事项：\n\n- `enabled`、`apiKey`、`baseUrl`、`model` 都必须完整配置，LLM 调用才会发生。\n- `baseUrl` 是服务根地址；hermes-repo 会请求 `{baseUrl}/chat/completions`。\n- 不直接支持 Anthropic 或 Gemini 原生接口。需要通过 OpenAI 兼容网关使用。\n- `.memory/config.json` 可能包含 `apiKey`，默认会被 gitignore。\n- 新项目默认开启 `consolidate.autoFlush.enabled`。LLM 配置完整后，capture 达到阈值时可以后台自动触发 `flush`。\n- 如果关闭 `autoFlush`，需要在积累 capture 后手动运行 `npx @riconext/hermes-repo flush`。\n\n手动处理排队的捕获升级：\n\n```bash\nnpx @riconext/hermes-repo capture-llm --flush\n```\n\n## MCP 服务器使用\n\nhermes-repo 提供了 MCP 服务器（`@riconext/hermes-mcp-server`）用于团队级别的记忆管理。它暴露 MCP 工具用于列出项目、添加记忆、搜索和提升记忆，同时提供 REST API 供 Web UI 使用。\n\n\nMCP 服务会在两个地方使用： \n\n- 执行 `flush` 时：程序会向 MCP 服务拉取团队记忆并推送个人记忆\n- 对话中：可以直接在对话中让编程工具调用 MCP 服务提供的工具拉取团队记忆到项目中或者推送记忆到服务中等\n\n为了能在 `flush` 环节能够推送和拉取团队记忆， 需要在 `init` 阶段配置 MCP 的服务。 \n\n有几个关键内容需要填写：\n\n```json\n\"serverUrl\": \"mcp 服务的地址\",\n\"projectId\": \"在 mcp 上面录入的当前项目的projectId\",\n\"userId\": \"在 mcp 上创建的当前项目的userId\",\n```\n\n为了能够在对话中自动获取 MCP 服务的工具， 也可以手动添加 MCP 服务, 该服务提供一下工具：\n\n### MCP 工具\n\n- `list_projects` — 列出可用项目\n- `add_memory` — 向项目添加新记忆\n- `search_memories` — 按关键词搜索记忆\n- `promote_memory` — 将记忆提升到团队级别\n- `delete_memory` — 删除记忆\n\n### 部署 MCP 服务器\n\nMCP 服务需要自行部署，才能够使用， 将项目拉取到本地， 进行一下操作：\n\n1. **启动 PostgreSQL**\n\n 在仓库根目录：\n\n ```bash\n docker compose up -d\n ```\n\n2. **配置环境变量**\n\n ```bash\n cd packages/mcp-server\n cp .env.example .env\n ```\n\n 关键变量：\n\n - `DATABASE_URL` — PostgreSQL 连接字符串\n - `MCP_TRANSPORT` — `httpStream`（默认）或 `stdio`\n - `DEV_AUTH_BYPASS=true` — 开发模式跳过 JWT 认证\n\n3. **初始化数据库**\n\n ```bash\n bun run db:push\n bun run db:seed\n ```\n\n 默认管理员账号：`admin` / `admin`（角色：SUPER_ADMIN）\n\n4. **启动 MCP 服务**\n\n ```bash\n bun run dev:mcp # 从仓库根目录\n # 或\n cd packages/mcp-server\n bun run dev\n ```\n\n 服务运行在 `http://localhost:3000`。健康检查：`http://localhost:3000/health`。\n\n### 接入 Claude Code\n\n在 Claude Code 配置中添加 MCP 服务器：\n\n```json\n{\n \"mcpServers\": {\n \"hermes\": {\n \"url\": \"http://localhost:3000/mcp\",\n \"headers\": {\n \"X-User-Id\": \"00000000-0000-4000-8000-000000000001\"\n }\n }\n }\n}\n```\n\n请将 `/path/to/hermes-repo` 替换为实际的仓库路径。\n\n\n## 部署 UI\n\nWeb UI（`@riconext/hermes-ui`）提供了用于浏览项目和记忆的仪表盘。\n\n1. **配置环境变量**\n\n ```bash\n cd packages/ui\n cp .env.example .env.local\n ```\n\n 编辑 `.env.local`：\n\n ```bash\n NEXT_PUBLIC_API_URL=http://localhost:3000\n ```\n\n2. **启动 UI**\n\n 从仓库根目录：\n\n ```bash\n bun run dev:ui\n ```\n\n 或从 UI 包目录：\n\n ```bash\n bun run dev\n ```\n\n 访问 UI：`http://localhost:3001`\n\n3. **生产构建**\n\n ```bash\n cd packages/ui\n bun run build\n bun run start\n ```\n\n> MCP 服务不是必须的， 但是如果你需要在团队中使用 `@riconext/hermes-repo` 的记忆管理能力， 就需要自行部署 MCP 服务了。\n","readmeFilename":"README.md"}