Building a Repository Analysis Tool: Lessons from Repomix and a Rust Prototype

This article details an in-depth analysis of Repomix’s innovative token-count-based repository insights, particularly its “Token Count Tree” feature. Inspired by Repomix’s approach, I’ve developed a preliminary prototype within my Rust CLI tool, aiming to replicate and expand upon similar analytical capabilities. Currently, this prototype enhances the summary view with two key insights: a comprehensive language breakdown by file extension (showing files, lines, megabytes, and percentage of total lines) and a “hotspot” view identifying top files by line count. While Repomix leverages OpenAI’s tiktoken models for token analysis, my initial Rust implementation uses line counts to establish a rapid, dependency-free proof-of-concept. This post shares my observations, relevant code references, and design principles gleaned from the project.

Understanding Repomix’s Token Insight Feature

Repomix offers powerful “Token Count Optimization” and a distinctive “token count tree” visualization. These features are documented within its README, which highlights options like --token-count-tree and summary configurations such as --top-files-len. The tool effectively integrates token metrics into its output summaries and hierarchical trees, facilitating efficient threshold filtering and the swift identification of code hotspots within a repository.

Architectural Deep Dive into Repomix

Despite being a TypeScript project, Repomix boasts a highly modular architecture. Its token counting and metric generation functionalities are distributed across several key modules.

Token Counting and Metrics:

• src/core/metrics/TokenCounter.ts (Link)
• src/core/metrics/tokenCounterFactory.ts (Link)
• src/core/metrics/calculateMetrics.ts (Link)
• src/core/tokenCount/buildTokenCountStructure.ts (Link)

Output Generation:

These modules handle the transformation of metrics into formatted output.

• src/core/output/outputGenerate.ts (Link)
• src/core/output/outputSort.ts (Link)

Configuration:

Various aspects, including output.topFilesLength, output.tokenCountTree, and tokenCount.encoding, are configurable via options detailed in the README’s “Configuration Options” section.

The Mechanics of Repomix’s Token Counting (High-Level)

At a high level, Repomix operates through a streamlined process:

• A TokenCounter abstract layer manages tokenization, offering flexibility to configure various encodings, such as o200k_base for GPT-4o.
• The tool constructs a detailed, structured representation of token counts for individual files and directories using buildTokenCountStructure.ts. This structure is crucial for generating the token count tree visualization and applying threshold-based filtering.
• Centralized metric computation occurs in calculateMetrics.ts. These metrics are subsequently fed to output generators responsible for rendering the data in formats like XML, Markdown, or JSON.
• User-defined configuration flags dictate the output, controlling elements such as the inclusion of summaries, the number of top files to display, and the presence of the token count tree.

Insights from Repomix’s Design

Analyzing Repomix’s codebase revealed several key design principles:

• Separation of Concerns: Token counting logic is distinctly separated from output generation through dedicated metrics modules, simplifying formatting and promoting metric reusability.
• Pluggable Encodings: The TokenCounter factory design allows for effortless switching between different tokenization models and encodings.
• Structured Tree Building: The token count tree is generated from a pre-computed, aggregated structure, which significantly streamlines sorting and threshold application rather than rendering on the fly.
• Configuration-Driven Output: A consistent set of metrics can be presented in diverse output styles with minimal conditional logic, driven entirely by configuration.

Strategies Used to Understand the Code

My strategy for navigating and understanding the Repomix codebase involved:

• Directly browsing relevant directories in the GitHub UI, specifically src/core/metrics, src/core/tokenCount, and src/core/output.
• Skimming the project’s README to establish a clear mapping between CLI flags and their corresponding modules.
• Executing targeted searches for specific file names and keywords such as TokenCounter and tokenCountTree to pinpoint implementations.
• Cross-referencing type definitions across different modules to meticulously trace the flow of data.

My Rust CLI Prototype: A Foundation for Future Analysis

I’ve developed a rapid proof-of-concept within my Rust CLI tool, focusing on line counts as an initial proxy for tokens. This functionality is implemented in src/output.rs and introduces two enhancements to the summary section:

• A detailed language breakdown by file extension, providing counts of files, lines, bytes, and the percentage of total lines.
• A list of the top 10 files by line count, offering a quick glimpse into major code contributors.

This approach was chosen for its pragmatism: it required no new external dependencies, leveraging existing FileContext data (lines and size). It effectively mirrors the essence of Repomix’s “top files” and “token-tree” features with a simpler metric, establishing a stable foundation for the eventual integration of actual token counting.

Future Enhancements and Roadmap

The roadmap for the Rust tool includes several planned improvements:

• Implementing CLI and configuration options to toggle these summary sections and customize list lengths (e.g., --top-files-len).
• Enhancing language detection capabilities, including mapping file extensions to canonical language names and potentially integrating a linguist-like detection system.
• Introducing true token counting functionality using a Rust-based tokenizer (such as tiktoken-rs or tokenizers), initially placed behind a feature flag.
• Developing a “Line Count Tree” with optional thresholding to mimic Repomix’s token-count-tree user experience, with a future plan to transition this to token counts.
• Expanding test coverage to deterministically validate the new summary content.

These planned enhancements are actively being tracked in the project’s issue tracker.

Open Questions and Considerations

As the project evolves, several key questions remain:

• Determining the primary tokenizer/encoding to support first (e.g., o200k_base versus cl100k_base).
• Establishing a robust strategy for handling binary and generated files within metric calculations, noting that Repomix typically excludes large or binary files by default.
• Deciding how best to present metrics in non-Markdown output formats like JSON or plain text.

Key Resources

• Repomix Repository: https://github.com/yamadashy/repomix
• Repomix Token Counter Implementation: https://github.com/yamadashy/repomix/blob/main/src/core/metrics/TokenCounter.ts
• Repomix Token Structure Building: https://github.com/yamadashy/repomix/blob/main/src/core/tokenCount/buildTokenCountStructure.ts
• Repomix Output Generator: https://github.com/yamadashy/repomix/blob/main/src/core/output/outputGenerate.ts