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CodeNib

Registre officiel Outils de développement

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

Code source Site web Documentation

Installation

Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).

PYPI package codenib
Claude Code 
claude mcp add codenib -- uvx codenib==0.2.4 mcp '<repository>'
Configuration JSON (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "codenib": {
            "command": "uvx",
            "args": [
                "codenib==0.2.4",
                "mcp",
                "<repository>"
            ]
        }
    }
}

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  1. Find the right code. Trace the calls.
  2. Quickstart
  3. Why CodeNib
  4. How it compares
  5. Languages
  6. Results and reproductions
  7. Documentation and community
  8. Citation

Find the right code. Trace the calls.

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.

Quickstart

Requires Python 3.10+, Git, a clean repository, and Claude Code or Codex.

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 follow callers, callees, definitions, and references.
  • 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, checked against upstream docs on 2026-09-28. This compares capabilities, not benchmark scores.

ToolAsk in plain languageCallers/callees beyond one hopRelationships resolved byRuns
grep / readNo; literal or regexNoText matchLocal
CodeNib CodeGraphYes; ranked source anchors (explore_context)Yes; one call, depth ≤ 8 (dependency_subgraph)SCIP/LSP indexers (compiler-resolved)Local
SerenaNo; symbol name or regexOne level per call (find_referencing_symbols)Live language server or IDELocal
CodeGraph (colbymchenry)Yes; FTS5 full-text (codegraph_explore)Yes; call paths in codegraph_exploreTree-sitter AST extractionLocal; telemetry opt-out
DeepWiki public MCPYes; generated answers (ask_question)No structured graph toolsGenerated WikiHosted

None of these repository tools needs its own model or API key; your agent uses its own model. Detailed comparison, sources and boundaries. CodeNib 0.2.4 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 hereWhat you can inspect
Context methods and support matrixEvery retrieval pipeline, LLM workflow and agent route, sorted by the views it reads and who controls the search
BenchmarksLocalization, 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 result100-issue retrieval comparison, candidate controls, model use and limitations
Agent integration matrixRevision-pinned LocAgent, Agentless, CoSIL, OrcaLoca and RepoNavigator contracts; compatibility does not imply reproduced paper scores
SWE-Explore validation1,020/1,020 real-output metric cells match the pinned official evaluator on a fixed 20-case run
DGX Spark deploymentLocal 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.

Questions fréquentes

Qu'est-ce que CodeNib ?

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

Comment installer CodeNib ?

Il s'exécute sur votre machine. Dans Claude Code : claude mcp add codenib -- uvx codenib==0.2.4 mcp '<repository>'. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.

Faut-il une clé API pour CodeNib ?

Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.

CodeNib est-il open source ?

Oui. Son code est sur GitHub sous licence Apache-2.0.

Qui maintient CodeNib ?

Il est publié par sysevol-ai. AI.tn le référence sans le maintenir ni le recommander.

Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.