What Compression Algorithms Does Headroom’s CodeCompressor Support for Different Languages?

Headroom’s CodeCompressor implements a single AST-aware compression algorithm that parses source code with tree-sitter and selectively trims function bodies while preserving imports, signatures, and type annotations, supporting Tier 1 languages (Python, JavaScript, TypeScript) and Tier 2 languages (Go, Rust, Java, C, C++).

The chopratejas/headroom repository provides an intelligent code compression system designed to reduce token counts for LLM context windows without breaking syntactic validity. Unlike traditional text compression methods, Headroom’s CodeCompressor uses Abstract Syntax Tree (AST) analysis to uniformly process multiple programming languages through a single algorithmic pipeline.

How the AST-Aware Compression Algorithm Works

According to the source code in headroom/transforms/code_compressor.py, the compression algorithm operates as a six-stage pipeline that guarantees syntactically valid output:

1. Language Detection: The system applies quick regex pre-filters followed by full tree-sitter parsing to identify the source language (lines 22-69).
2. AST Configuration Extraction: It retrieves language-specific configurations for imports, function definitions, class structures, and decorators (lines 29-86).
3. Symbol Importance Analysis: The algorithm calculates reference counts, analyzes call graphs, and measures context relevance to determine which symbols are essential (lines 108-156).
4. Budget Allocation: It allocates a body-line budget based on the target compression rate to determine how much content can be removed (lines 68-89).
5. Selective Compression: The system trims function and class bodies while preserving imports, signatures, type annotations, and decorators (lines 94-115).
6. Syntax Validation: Finally, it re-assembles the code and verifies validity through a second tree-sitter parse (lines 124-130).

Language Detection and Parsing

The algorithm begins by detecting the programming language using the CodeLanguage enum defined in headroom/transforms/code_compressor.py. The detection mechanism uses regex pre-filters to quickly identify file types before invoking the appropriate tree-sitter grammar for full AST parsing. This ensures that language-specific constructs like Python decorators or TypeScript interfaces are correctly identified and preserved during compression.

Symbol Importance Analysis

During the symbol analysis phase, the compressor builds a reference count matrix and call graph to identify which functions and classes are most critical to the codebase. Symbols with high reference counts or those appearing in public APIs receive preferential treatment in the body-line budget allocation, ensuring that the most important code remains readable while peripheral utility functions are compressed more aggressively.

Supported Languages (Tier 1 and Tier 2)

The CodeLanguage enum in headroom/transforms/code_compressor.py (lines 15-26) defines a tiered support structure for programming languages:

Tier 1 (core support):

• Python
• JavaScript
• TypeScript

Tier 2 (extended support):

• Go
• Rust
• Java
• C
• C++

Regardless of tier, all languages use the same AST-aware compression algorithm applied through their respective tree-sitter grammars. The tier classification reflects the depth of testing and optimization rather than different compression methodologies.

Practical Usage Examples

Python Implementation

The CodeAwareCompressor class exposed in headroom/transforms/__init__.py provides a Python interface for compressing code:

from headroom.transforms import CodeAwareCompressor

code = """
import os
from typing import List

def process_data(items: List[str]) -> List[str]:
    """Process a list of items."""
    results = []
    for item in items:
        if not item:
            continue
        processed = item.strip().lower()
        results.append(processed)
    return results
"""

compressor = CodeAwareCompressor()
result = compressor.compress(code)          # Auto-detects Python

print(result.compressed)                    # Valid, shortened Python

print(f"Saved {result.savings_percentage:.1f}% tokens")

The compressor automatically detects Python, parses the AST, and compresses the function body while preserving the import statements and docstring.

TypeScript and Node.js Implementation

The TypeScript SDK exposes the same CodeAwareCompressor class for Node.js environments:

import { CodeAwareCompressor } from "headroom-ai";

const code = `
import { readFile } from "fs";

export function transform(data: string[]): string[] {
  // Complex processing ...
  return data.map(d => d.trim().toUpperCase());
}
`;

const compressor = new CodeAwareCompressor();
const result = await compressor.compress(code, { language: "typescript" });

console.log(result.compressed);
console.log(`Saved ${result.savings_percentage}% tokens`);

Passing the language: "typescript" hint ensures the TypeScript parser is used, though the same underlying AST-aware algorithm applies the compression logic.

Key Implementation Files

The following files define the compression algorithm and language support in the chopratejas/headroom repository:

• headroom/transforms/code_compressor.py: Core implementation containing the CodeLanguage enum, detection logic, and the six-stage compression pipeline.
• headroom/transforms/__init__.py: Public API surface exporting CodeAwareCompressor and CodeCompressorConfig.
• tests/test_transforms/test_code_compressor.py: Comprehensive test suite validating language detection and compression behavior across all supported languages.

Summary

• Headroom uses a single AST-aware compression algorithm applied uniformly across all supported languages rather than language-specific compression schemes.
• The algorithm leverages tree-sitter for parsing and validation, ensuring syntactically valid output even after aggressive compression.
• Tier 1 support includes Python, JavaScript, and TypeScript, while Tier 2 support covers Go, Rust, Java, C, and C++.
• The compression process preserves critical structural elements including imports, function signatures, type annotations, and decorators while trimming function bodies based on symbol importance.
• Implementation resides primarily in headroom/transforms/code_compressor.py with public access through the CodeAwareCompressor class.

Frequently Asked Questions

What compression algorithm does Headroom use for code?

Headroom uses a single AST-aware compression algorithm that parses source code with tree-sitter, analyzes symbol importance through reference counting and call graphs, and selectively trims function bodies while preserving imports and signatures. This algorithm is applied uniformly across all supported languages rather than using different compression schemes for each language.

Which programming languages does CodeCompressor support?

CodeCompressor supports Tier 1 languages (Python, JavaScript, TypeScript) with core optimization, and Tier 2 languages (Go, Rust, Java, C, C++) with extended support, as defined in the CodeLanguage enum in headroom/transforms/code_compressor.py (lines 15-26).

How does CodeCompressor ensure the compressed code remains syntactically valid?

After compressing function bodies and re-assembling the source code, the algorithm performs a second tree-sitter parse (lines 124-130 in code_compressor.py) to verify that the output is syntactically valid. This validation step ensures that the compression process never introduces syntax errors or breaks the code structure.

Can I configure the compression ratio for different languages?

Yes, the compression ratio is configurable through the body-line budget allocation system (lines 68-89), which calculates how many lines of code to preserve based on a target compression rate. While the algorithm remains the same across languages, the budget parameters can be adjusted to control the aggressiveness of compression for specific codebases.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}