CodeNib
概览
作者提供的说明（英文原文）
Source-linked CodeGraph exploration, ranked search, and static navigation for coding agents.
PopNx MCP 的三大支柱
这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i3 项中通过 3 项依据公开信息检查；尚未扫描代码
- 使用情况i起步阶段↓ 327/周 · ★ 91
它能帮你做什么
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
- 连接方式: 在你的电脑上运行
- 传输方式: stdio
- 分类: 搜索与 AI 记忆
安装
选择你使用的 AI 应用，然后复制设置。
安装之前
- 基础检查：3 项中通过 3 项
- 在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
- 注册中心没有列出需要填写的密钥。
你需要
- PyPI已安装 Python 的 uv（用于 uvx 命令）
你用的是哪个应用？
在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"codenib": {
"command": "uvx",
"args": [
"codenib",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add codenib -- uvx codenib mcp这个服务器需要在启动命令中填入值，请复制设置后自行填写。
或者自己复制设置
粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json
{
"mcpServers": {
"codenib": {
"command": "uvx",
"args": [
"codenib",
"mcp"
]
}
}
}浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。
这个服务器需要在启动命令中填入值，请复制设置后自行填写。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"codenib": {
"type": "stdio",
"command": "uvx",
"args": [
"codenib",
"mcp"
]
}
}
}浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。
这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上，无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。
它能做什么
它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日
- ✓ 通过
作者与仓库一致
注册名称已通过域名 codenib.ai 验证（注册中心会核实域名所有权）
- ✓ 通过
仍在维护
代码最后更新于4天前
- ✓ 通过
有许可证
使用 Apache-2.0
- – 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表
与“搜索与 AI 记忆”中的其他服务器比较i
- 星标数多于此分类中其他 5747 个服务器的 100%
- 发布到注册中心的时间比此分类中其他 5747 个服务器的 88% 更新
- 基础检查结果等于或优于此分类中其他 5747 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。
常见问题
CodeNib 安全吗？
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。
它需要哪些密钥或代码？
注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。
它在我的电脑上运行还是在云端？
它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。
它还在维护吗？
GitHub 仓库最后更新于4天前，最新版本（0.2.5）于5天前进入注册中心。
哪些应用可以使用它？
支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
有什么可以替代它？
同一分类中有：Context7, Codebase Memory, Exa
替代选择
同一分类中其他作者的服务器，按星标数排序。
如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Find the right code. Follow its references. Give Claude Code and Codex source context and typed graph navigation across languages.
71.4% code-block Recall@5 on 100 real issues.
Model-planned grep → Jev: +12.8 percentage points over the same grep candidates without reranking.
Experimental result · Method and limitations
Use with your agent · Browse a Wiki · Languages · Reproductions · Releases
▶ Watch the 15-second replay · real CLI output · waits condensed · Pinned source, transcript and setup details .
New in 0.2.5: saved repository Wikis and portable OKF export, Pyrefly-backed Python graphs, static entry-path navigation and call-path rule checks. Read the release notes.
Quickstart
Requires Python 3.10+, Git, and Claude Code or Codex. Initialization accepts uncommitted edits and preserves the checkout.
python -m pip install "codenib[graph,mcp]"codenib codegraph init /path/to/your/repo
Then ask your agent: “Use CodeNib's `explore_context` to find where request retry behavior is implemented. Cite the files and lines.”
This shipping path builds local search and a typed symbol graph, then connects installed agent clients. It needs no model, API key, or GPU for CodeNib; your agent uses its own model. It is separate from the grep/Jev experiment above. Language toolchain and project prerequisites vary.
Setup and troubleshooting · Source exclusions · All MCP tools · Local Wiki
Why CodeNib
- Inspect the evidence. Retrieve bounded code context with file and line references, then navigate definitions and static references. Read the source at each reference before describing a call path.
- Use the same tools across languages. The registry tracks 14 language entries, including 12 with graph backends. Check capability and setup differences in the language matrix.
- Compare methods on the same source. Pinned agent contracts, datasets, and scorer checks make the reproduction surface inspectable.
- Share what you found. Export a Wiki with source citations to your own GitHub Pages, or browse the public examples.
How it compares
What an agent can ask each tool. Peer interfaces were checked against upstream docs on 2026-09-28. This compares capabilities, not benchmark scores.
| Tool | Ask in plain language | Relationships beyond one hop | Relationships resolved by | Runs |
|---|---|---|---|---|
| grep / read | No; literal or regex | No | Text match | Local |
| CodeNib CodeGraph | Yes; ranked source anchors (explore_context) | Incoming/outgoing static references; one dependency_subgraph call, depth ≤ 8 | SCIP/LSP indexers (compiler-resolved) | Local |
| Serena | No; symbol name or regex | One level per call (find_referencing_symbols) | Live language server or IDE | Local |
| CodeGraph (colbymchenry) | Yes; FTS5 full-text (codegraph_explore) | Yes; call paths in codegraph_explore | Tree-sitter AST extraction | Local; telemetry opt-out |
| DeepWiki public MCP | Yes; generated answers (ask_question) | No structured graph tools | Generated Wiki | Hosted |
CodeNib's indexed references can include calls, fields, type uses and function values. Inspect the source to establish which references invoke a function; external and dynamic relationships may be missing. For a known string or file, your agent's native search and reads remain useful. Include indexing and rebuild costs when assessing graph navigation on your own tasks.
None of these repository tools needs its own model or API key; your agent uses its own model. Detailed comparison, sources and boundaries. CodeNib also includes an optional grep → Jev route using OpenRouter for planning and reranking; selected code goes to remote models. Authorization previews remain opt-in. The historical research result above is separate from the product evaluation and the model-free CodeGraph row.
Languages
Graph backends: Python, Go, Rust, C/C++, C#, Java, Ruby, PHP, Kotlin, Scala, JavaScript, and TypeScript. Swift and Lua support chunking/retrieval. Provider prerequisites and coverage differ by language.
The generated language matrix separates chunking, graph backends, incremental-backend support, and decoder parity. The product currently reuses or rebuilds views; file-level delta repair is not enabled.
Results and reproductions
| Start here | What you can inspect |
|---|---|
| Context methods and support matrix | Every retrieval pipeline, LLM workflow and agent route, sorted by the views it reads and who controls the search |
| Benchmarks | Localization, agent-context and resolution layers, evidence levels, and the method × benchmark matrix over CodeNib Base/Synthesis, SWE-bench Lite, Loc-Bench, SWE-Explore, Agent Retrieval Bench and ContextBench |
| grep → Jev result | 100-issue retrieval comparison, candidate controls, model use and limitations |
| Agent integration matrix | Revision-pinned LocAgent, Agentless, CoSIL, OrcaLoca and RepoNavigator contracts; compatibility does not imply reproduced paper scores |
| SWE-Explore validation | 1,020/1,020 real-output metric cells match the pinned official evaluator on a fixed 20-case run |
| DGX Spark deployment | Local Wiki, CodeGraph and model serving on GB10; a deployment guide, not a token-saving benchmark |
Documentation and community
Documentation · Architecture · Contributing · CI and testing · Changelog · Discord
CodeNib is in beta; public interfaces may change before a stable release. Historical research artifacts retain their published dataset identifiers.
Citation
If you use CodeNib in your research, please cite our arXiv paper:
@misc{yu2026codenibmultiviewdataserving,title={CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents}, author={Zhongming Yu and Hengjia Yu and Boqin Yuan and Shuting Zhao and Yizhao Chen and Aryan Dokania and Mihir Jagtap and Jiayu Chang and Yitong Ma and Yash Jayswal and Wentao Ni and Hejia Zhang and Zhaoling Chen and Gangda Deng and Jishen Zhao}, year={2026}, eprint={2607.25431}, archivePrefix={arXiv}, primaryClass={cs.SE}, url={https://arxiv.org/abs/2607.25431}, }
CodeNib is licensed under Apache 2.0.