RepoWeaver
RepoWeaver
面向 AI 编码代理的代码上下文织物 — 确定性调用图索引、混合检索和单一工具 MCP 接口。
RepoWeaver 构建了一个本地、零成本的代码智能层,让 AI 编码代理(Codex、Claude Code、CodeWiz 等)无需读取整个文件即可回答结构性问题。
"Who calls this method, and what would break if I change its signature?"
→ explore(query, task="impact", repo=".", max_tokens=4000)
→ [verbatim source slices + call paths + blast radius + known blind spots]为什么
依赖 grep 或全文读取的 AI 代理会浪费 token,并遗漏跨文件调用链。 现有工具要么需要云 API,要么在索引层嵌入 LLM,要么带有限制性许可证。 RepoWeaver 只从该领域中采纳已验证的共识模式,并在本地组装它们。
Related MCP server: codemap
设计
六个原语,均已在多个开源工具中得到独立验证:
# | 原语 | 验证方 |
1 | tree-sitter 确定性解析,本地 $0 | CodeGraph · GitNexus · Graft · Aider |
2 | 调用 / 继承 / 导入边 + 爆炸半径 | CodeGraph · GitNexus · Graft · CodeQL |
3 | BM25/FTS + PageRank 图扩散 | Graft · CodeGraph · Aider |
4 | 内容哈希增量新鲜度 | Graft · Codebase-Memory · CodeGraph |
5 | MCP 单一强工具 | CodeGraph(经验验证) |
6 | AGENTS.md 协议注入 + 编辑钩子 | GitNexus · CodeGraph · Graft · Potpie |
不包括:索引层中的 LLM(所有严肃工具均拒绝)、发送到外部 API 的代码、主观语义摘要。
路线图
里程碑 | 标签 | 状态 |
M1 — Fabric MVP(Java 解析器 + 边 + FTS5 + PageRank + MCP) | v0.1.0 | 🔜 |
M2 — 新鲜度与置信度(自动同步 + 消歧 + 置信度边) | v0.2.0 | 计划中 |
M3 — 类型精度覆盖层(SCIP/jdtls) | v0.3.0 | 计划中 |
M4 — 运行时覆盖层(OTel/Jaeger 追踪 → 边权重) | v0.4.0 | 计划中 |
每个里程碑都附带一个在 CI 中运行的 fabric verify --level mN 门禁。
验证方法
基准测试仅使用公共仓库 — 没有专有代码进入此仓库。
基准仓库 | 目的 |
| Spring 依赖注入密集,测试注入感知边 |
| 与 CodeGraph 公布的 93.3% Java 覆盖率相当 |
| 调用链深度 |
| 生成代码噪声过滤 |
安装(即将在 v0.1.0 中推出)
pip install repoweaver
fabric build # index current repo, $0, no LLM
fabric init # inject AGENTS.md protocol + edit hook
fabric verify --level m1MCP 工具
explore(
query: str,
task: "understand" | "impact" | "locate" | "debug",
repo: str = ".",
max_tokens: int = 4000
) → {
slices: [{file, span, source}],
call_paths: [...],
blast_radius: [...],
blind_spots: "DI / reflection / MQ edges not represented"
}致谢
设计模式源自(并非派生自): CodeGraph · Graft · GitNexus · Serena · SCIP · Aider
许可证
MIT
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