Understand unfamiliar code with evidence.

Read a Wiki of how a repository works, follow its call paths on a map, and open the source behind every explanation.
Then give Claude Code or Codex the same index.

Browse a Wiki  ·  Use with your agent  ·  Docs  ·  Languages  ·  Releases

_{The Requests Wiki. Each node on the map is a page, and the heavy lines are references the code index recorded between areas. The page cites a file and line range for each claim.}

New in 0.2.6: Wikis open as a map of their pages and lead with the main path through the code, faster graph builds, and first C/C++ and JavaScript/TypeScript builds that leave the checkout alone. Read the release notes.

A Wiki you can follow down to the source

CodeNib indexes a repository, groups its code into areas, and writes a Wiki page for each one. The map and the call paths come from the code index, so you can check every arrow against the source.

• Start from the map. Pages are nodes. Drag to pan, scroll to zoom, and click a node to read its page. Click a heavy line to read an example of the references behind it.
• Follow one path through the code. The Overview opens on the main path through the repository, one sentence per recorded call. The most important detail pages draw their longest recorded call path. No model chooses the arrows.
• Check each claim. Sentences end with the file and line range they come from, and each page lists its callers and calls in the code graph.
• Ask a follow-up. Ask this repository answers a question with source citations, using the model you configure.

_{The gin Wiki opens on one recorded path, from GET() to node.insertChild().}

Open a Wiki

To	Do this
Read one now	Open a ready Wiki for requests, scikit-learn, sympy, fmt, redis, caddy, gin, hugo and more. No account or API key.
Map a public GitHub repository	Paste its URL on demo.codenib.ai, or replace github.com with demo.codenib.ai in the URL. A new Wiki uses your OpenRouter account, with a $0.25–$5 spending limit you set before it starts.
Map your own checkout	Run the Wiki locally, as below.
Share it	Publish the Wiki, with its source citations, to GitHub Pages.

To build a Wiki with the map on your machine:

python -m pip install "codenib[graph]"
codenib wiki /path/to/your/repo --preset graph

This builds a search index and a symbol graph, then opens the Wiki at http://localhost:3000. Pages are written from the index without a model. Add --generate --model <provider/model> for model-written pages. Local Wiki guide

Use it with your coding agent

Build the same index for Claude Code or Codex. Your agent can then find definitions, follow callers and read the source in your own checkout.

_{One recorded Claude Code session on Requests: 14.1 s, 3 turns, 2 CodeNib tools. ▶ Watch it replay · Pinned source, transcript and setup details}

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 builds local search and a typed symbol graph, then connects installed agent clients. CodeNib needs no model, API key, or GPU of its own; your agent uses its own model. Language toolchain and project prerequisites vary.

Setup and troubleshooting · Source exclusions · All MCP tools · Change guard (preview, Python: lists the tests a change reaches before your agent finishes)

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.

Languages

CodeNib registers 14 languages, 12 of them with a code graph.

Support	Languages
Code graph with an incremental backend (LSP or clangd)	Python, Go, Rust, C/C++, JavaScript, TypeScript
Code graph	C#, Java, Ruby, PHP, Kotlin, Scala
Search only, no definitions or callers	Swift, Lua

These are registered capabilities, not the tools on your machine; codenib toolchain status shows those. The product currently reuses or rebuilds views; file-level delta repair is not enabled. The language matrix lists each backend and its prerequisites.

Results and reproductions

• #1 in line recall and Pass@1 on ContextBench's Agent systems leaderboard: 77.90% line recall and 388 of 500 tasks resolved (77.60% Pass@1), as of October 23, 2026 (UTC). This is our research agent, not the Wiki or the code graph: two Claude Haiku 5.5 workers search with glob, grep and reads, then Claude Opus 5.5 repairs. How it works · Scope and evidence
• 71.4% code-block Recall@5 on 100 real issues with model-planned grep → Jev, 12.8 percentage points above the same grep candidates without reranking. This is an experimental retrieval route, separate from the model-free CodeGraph. Method and limitations

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
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.