问HN：上下文构建方法的最新进展
嗨，HN，我是一个非常基础的人工智能用户（我使用的是带有默认设置的VSCode聊天，没有使用任何MCP来构思计划），但最近我遇到了一些构建上下文的MCP/工具，它们可以为你的代码库构建知识图谱。
这非常有趣，因为我同时在处理相当大的代码库，想到我的代理不需要一次又一次地重新索引和重新读取我的所有文件，这让我感到很吸引。
我的问题是，你们使用什么方法来索引这些内容并将其连接到你的代理上？
我的研究让我找到了以下几个工具，但我对如何衡量它们的优劣感到非常困惑。
1. https://github.com/abhigyanpatwari/GitNexus - 似乎是最受欢迎的
2. https://github.com/DeusData/codebase-memory-mcp - 也很有趣/新颖
3. https://github.com/JaredStewart/coderlm - 基于tree-sitter的方法看起来非常不错。
我很想知道那些积极使用前沿模型和方法的人是如何在这个领域中导航的。
查看原文
Hi HN, I am a very minimal AI user (I use stock vscode chat with no MCPs to ideate on plans), but recently I've been coming across context building MCPs/tools that build a knowledge graph of your codebase.<p>This is really interesting because I am working on pretty large codebases simultaneously and the idea that my agent won't have to re-index and re-read all my files over and over again is pretty enticing.<p>My question is what methods are you using to index and connect these to your agents?<p>My research has led me to the following but I am basically very confused by what metric to measure how good they are.<p>1. https://github.com/abhigyanpatwari/GitNexus - seems like the most popular<p>2. https://github.com/DeusData/codebase-memory-mcp - also interesting/new<p>3. https://github.com/JaredStewart/coderlm - the tree-sitter based approach seems really good.<p>Would love to know how people actively using frontier models and methods navigate this domain.