CodeNib

Name: CodeNib
Author: sysevol-ai

by sysevol-aiversion 0.2.5i★ 91iRuns on your computer

Registry nameai.codenib/codenib

Overview

Description from the maker (original English)

Source-linked CodeGraph exploration, ranked search, and static navigation for coding agents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi3 of 3 passedChecked from public information; code not scanned yet
UsageiVery new★ 91

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

You need

PyPIPython’s uv installed (for the uvx command)

Claude DesktopClaude CodeCursorVS Code

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "codenib": {
      "command": "uvx",
      "args": [
        "codenib",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add codenib -- uvx codenib mcp

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "codenib": {
      "command": "uvx",
      "args": [
        "codenib",
        "mcp"
      ]
    }
  }
}

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "codenib": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "codenib",
        "mcp"
      ]
    }
  }
}

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 5, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain codenib.ai (the registry checks domain ownership)
✓ Pass
Still maintained
Code last changed today
✓ Pass
Has a licence
Uses Apache-2.0
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Needs the server’s tool list first; this check starts in the next step

Compared with others in Search & AI memoryi

More stars than 0% of the other 10 servers in this category
Published to the registry more recently than 90% of the other 10 servers in this category
Basic checks equal to or better than 100% of the other 10 servers in this category

Compared only with servers in this prototype, not the whole registry.

Common questions

Is CodeNib safe?

It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed today, and the latest version (0.2.5) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 2 days ago

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 19 days ago

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 43 days ago

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.