From the big picture to the source
Understand a repo.
Follow the code.
Every repository is mapped from its code index: its parts, the calls between them, and the path a request takes through them. Follow any line down to the source.
Or replace github.com with demo.codenib.ai in any repository URL.
From the map to the line
Ask one question. Land on the exact lines.
Each step is a call the index recorded. Click one to read the source it points at. No account or API key needed.
Take that context to your coding agent.
Explore your own checkout with Claude Code or Codex.
python -m pip install "codenib[graph,mcp]"
codenib codegraph init /path/to/your/repoPython 3.10+, Git, a clean checkout, and Claude Code or Codex. This shipping CodeGraph path runs locally without a model, API key, or GPU for CodeNib; your agent uses its own model. It is separate from the grep/Jev experiment.
Setup and language prerequisites · Ask your agent to start with explore_context.
Evidence you can inspect. Tools you can compare.
Follow source locations, check language coverage, and reproduce the retrieval methods.
Follow definitions and calls
Bounded context and typed SCIP/LSP graphs help your agent navigate from a question to the source. Compare the fit with grep, Serena, CodeGraph, and DeepWiki.
Compare repository tools →Know your language's support
14 language entries, 12 with graph backends. See which providers, prerequisites, and capabilities apply to your repository.
Open the language matrix →Reproduce the methods
Pinned LocAgent, Agentless, CoSIL, OrcaLoca, and RepoNavigator contracts. Dataset and scorer checks make the evaluation boundaries visible.
Explore agent integrations →Share a Wiki from your own repository with the existing GitHub Pages workflow.
71.4% code-block Recall@5.
Model-planned grep → Jev on 100 real issues. +12.8 points over the same grep candidates without reranking.
Experimental result · Method and limitations
From the blog
Implementation notes and measured tradeoffs in source-linked repository analysis.
Let a Model Plan grep, Then Let Jev Rank the Code
BM25, dense embeddings, hybrid retrieval, and model-planned grep on 100 CodeNib Base issues: accuracy, latency, and cost across complete retrieval and reranking paths.
Read the article →