codeix

codeix.dev · Fast semantic code search for AI agents — find symbols, references, and callers across any codebase.

codeix                 # start MCP server, watch for changes
codeix build           # parse source files, write .codeindex
codeix -r ~/project build  # build from a specific directory

Why

AI coding agents spend most of their token budget finding code before they can work on it. They grep, read files, grep again, backtrack. On a large codebase the agent might burn thousands of tokens just locating the right function — or worse, miss it entirely and hallucinate.

Codeix gives the agent a pre-built map of your codebase. One structured query returns the symbol name, file, line range, signature, and parent — no scanning, no guessing.

What existing tools get wrong

What codeix does differently

• Committed to git — the index is a .codeindex directory you commit with your code. Clone the repo, the index is already there. No re-indexing.
• Shareable — library authors can ship .codeindex in their npm/PyPI/crates.io package. Consumers get instant navigation of dependencies.
• Composable — the MCP server auto-discovers dependency indexes and mounts them. Query your code and your dependencies in one place.
• Structured for LLMs — symbols have kinds, signatures, parent relationships, and line ranges. The agent gets exactly what it needs in one tool call instead of piecing it together from raw text.
• Prose search — search --scope text targets comments, docstrings, and string literals specifically. Find TODOs, find the error message a user reported, find what a function's docstring says — without noise from code.
• Fast — builds in seconds, queries in milliseconds. Rust + tree-sitter + in-memory SQLite FTS5 under the hood.

The .codeindex format

An open, portable format for structured code indexing. Plain JSONL files you commit alongside your code — git-friendly diffs, human-readable with grep and jq, no binary blobs.

.codeindex/
  index.json        # manifest: version, name, languages
  files.jsonl       # one line per source file (path, lang, hash, line count)
  symbols.jsonl     # one line per symbol (functions, classes, imports, with signatures)
  texts.jsonl       # one line per comment, docstring, string literal

Any tool that can parse JSON can consume a .codeindex. Codeix builds it using tree-sitter, and AI agents query it through MCP (Model Context Protocol).

Example — symbols.jsonl:

{"file":"src/main.py","name":"os","kind":"import","line":[1,1]}
{"file":"src/main.py","name":"Config","kind":"class","line":[22,45]}
{"file":"src/main.py","name":"Config.__init__","kind":"method","line":[23,30],"parent":"Config","sig":"def __init__(self, path: str, debug: bool = False)"}
{"file":"src/main.py","name":"main","kind":"function","line":[48,60],"sig":"def main(args: list[str]) -> int"}

Ship your index with your package

Include .codeindex in your package and every developer who depends on you gets instant navigation of your API — no setup, no re-indexing.

Works with Git repos, n

... View full README on GitHub