
一个独立的本地专用 Python MCP 服务器,为任何 IDE 提供持久且与工作空间隔离的内存。可支持任何支持 MCP 服务器的 IDE
概览
⭐ 收藏项目:如果你使用该工具,请添加一个星级将其保存到GitHub个人资料,并跟踪新的性能更新。 永远不要再丢失你的 AI 上下文。zerikai_memory 为每个 IDE 提供持久、与工作空间隔离的内存,支持本地优先、成本意识和即时性。它使用确定性树状代码解析索引,将实体和深度代码描述(如函数、类和文档字符串)捕获到本地 ChromaDB 向量存储中。通过本地 MCP 接口访问,在保持高分辨率代码库映射的同时降低令牌成本,通过 L2 和词汇重新索引,严格进行源验证(实体、文件、行号和 L2)查询时获取高度相关上下文。设计上完美配合低成本的 DeepSeek API,注入结构化、高度精确的本地上下文,而非倾倒原始、庞大的文件,最大化 KV 缓存,从而大幅降低活跃令牌成本。 💡 状态:活跃且自成一体。
概述(AI 翻译)
zerikai_memory 🧠
⭐ 收藏项目:如果你使用该工具,请添加一个星级将其保存到GitHub个人资料,并跟踪新的性能更新。
永远不要再失去你的AI上下文。 zerikai_memory为每个IDE提供持久、工作空间隔离的内存,且本地优先、成本意识强且即时。它采用确定性树置代码解析索引,将实体和深度代码描述(如函数、类和文档字符串)捕获到本地ChromaDB向量存储中。通过本地MCP接口访问,在保持高分辨率代码库映射的同时大幅降低令牌成本,通过L2和词汇重新索引,严格验证源代码(实体、文件、行号和L2)获取高度相关的上下文。设计上与低成本的DeepSeek API完美匹配,注入结构化、高度精确的本地上下文,而非大量倾倒原始文件,最大化KV缓存命中率,显著降低活跃令牌成本。
💡 状态:活跃且自成一体。 该项目每日使用,作者积极维护。拉取请求和问题关闭,以降低维护开销。
本页由 AI 翻译,技术细节请以原文为准。
README
以下为原文 README 节选,完整内容请查看原文。
zerikai_memory 🧠
⭐ Bookmark the project: If you use this tool, drop a star to save it to your GitHub profile and track new performance updates.
Never lose your AI context again. zerikai_memory provides persistent, workspace-isolated memory for every IDE that is local-first, cost-aware, and instant. It uses deterministic Tree-Sitter code parsing indexing to capture entities and deep code descriptions like functions, classes, and docstrings into a local ChromaDB vector store. Accessed via a local MCP interface to slash token costs while maintaining high-resolution codebase mapping, it retrieves hyper-relevant context on query through L2 and Lexical re-indexing with strict source verification (Entity, File, Line Number, and L2). Designed to pair perfectly with low-cost DeepSeek APIs, it injects structured, highly precise local context instead of dumping raw, massive files, maximizing KV cache hits to radically reduce your active token costs.
💡Status: Active & Self-Contained. This project is used daily and actively maintained by the author. Pull Requests and Issues are closed to keep maintenance overhead low. It is provided fully functional and ready for production use.
⚠️ Platform Support Notice:
Note: This project is developed and actively maintained on Windows. GitHub Actions verifies that basic installation compiles across Windows, macOS, and Linux, but the maintainer cannot troubleshoot platform-specific runtime errors on Mac or Linux. Community pull requests fixing Mac/Linux bugs are highly welcome!
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →配置
{
"mcpServers": {
"universal-brain": {
"command": "C:\\path\\to\\zerikai_memory\\venv\\Scripts\\python.exe",
"args": ["C:\\path\\to\\zerikai_memory\\main.py"]
}
}
}