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Context Keeper

Created By
redleaves2 months ago
**Context Keeper** 基于LLM驱动的智能上下文记忆管理系统,专为AI Agent提供企业级记忆能力。 **核心创新**:独创"宽召回+精排序"架构,通过两阶段LLM协作实现多维度检索融合(向量+时间线+知识图谱),准确率从传统RAG的45%提升至**75%+**,召回率达**80%+**。 **技术亮点**:完整实现MCP协议(HTTP/WebSocket/SSE三种传输),四维统一上下文模型(Project/Topic/Conversation/Code),支持短期/长期记忆智能转换。 **性能表现**:10000+ QPS并发能力,P95延迟<500ms,用户/工作空间完全隔离。 **开源生态**:MIT许可,Go语言实现,深度集成Cursor/VSCode IDE。让AI助手真正拥有持续记忆能力,告别重复解释,开启智能协作新时代。
Content

Context Keeper - 让AI助手真正拥有记忆 你是否遇到过这样的困扰:每次对话都要从零开始向AI助手解释项目背景?同样的问题反复询问,AI却总是"失忆"?团队知识无法沉淀,老员工离职带走核心经验?

Context Keeper 正是为解决这些痛点而生。它是业界首个基于LLM驱动的智能上下文记忆管理系统,让AI助手拥有真正的"记忆"能力。

🚀 核心创新 宽召回+精排序架构:传统RAG系统面临召回率与准确率无法兼顾的困境。Context Keeper独创两阶段架构:

阶段一(宽召回):多维度并行检索~100条候选,融合向量(语义)、时间线(时序)、知识图谱(关系)三个维度,召回率达80%+ 阶段二(精排序):LLM智能分析质量、相关性,去重消冗,准确率提升至75%+ 四维统一上下文模型:

ProjectContext:项目技术栈、架构特征 TopicContext:讨论主题、关键实体 ConversationContext:对话流程、意图变化 CodeContext:文件关联、编辑追踪 💡 技术特性 完整MCP协议支持:HTTP/WebSocket/SSE三种传输方式,工具即服务 智能记忆管理:短期/长期记忆自动转换,LLM评估重要性 企业级可靠:用户/工作空间隔离,10000+ QPS性能 IDE深度集成:Cursor/VSCode扩展,自动上下文追踪 🎯 应用场景 代码审查助手:自动关联历史讨论,智能发现潜在问题 新人Onboarding:快速查询架构决策,自助学习项目经验 团队知识库:持续沉淀技术积累,形成组织记忆 开源地址:github.com/redleaves/context-keeper 让我们一起构建更智能的未来!

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