headroom/docs/content/docs/code-compression.mdx

---
title: Code Compression
description: AST-aware compression that preserves imports, signatures, and types while compressing function bodies. Powered by tree-sitter.
---

Headroom's CodeAwareCompressor uses tree-sitter to parse source code into an AST, then selectively compresses function bodies while preserving the structural elements that LLMs need -- imports, signatures, type annotations, and error handlers.

## Why AST-Aware Compression?

Naive truncation breaks code. Cutting a function in half leaves invalid syntax that confuses the LLM. CodeAwareCompressor guarantees:

- **Syntax validity** -- output always parses correctly
- **Structural preservation** -- imports, signatures, types, decorators are kept intact
- **Lightweight** -- ~50MB (tree-sitter) vs ~1GB for LLMLingua

## Supported Languages

| Tier | Languages | Support Level |
|---|---|---|
| Tier 1 | Python, JavaScript, TypeScript | Full AST analysis |
| Tier 2 | Go, Rust, Java, C, C++ | Function body compression |

## What Gets Preserved vs Compressed

**Always preserved:**
- Import statements
- Function and method signatures
- Class definitions
- Type annotations
- Decorators
- Error handlers (`try`/`except`, `try`/`catch`)

**Compressed:**
- Function bodies (implementations)
- Comments (unless configured to preserve)
- Verbose docstrings (configurable: full, first line, or removed)

## Example

```python
from headroom.transforms import CodeAwareCompressor

compressor = CodeAwareCompressor()

code = '''
import os
from typing import List

def process_items(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
'''

result = compressor.compress(code, language="python")
print(result.compressed)
# import os
# from typing import List
#
# def process_items(items: List[str]) -> List[str]:
#     """Process a list of items."""
#     results = []
#     for item in items:
#     # ... (5 lines compressed)
#     pass

print(f"Compression: {result.compression_ratio:.0%}")  # ~55%
print(f"Syntax valid: {result.syntax_valid}")  # True
```

## Configuration

```python
from headroom.transforms import CodeAwareCompressor, CodeCompressorConfig, DocstringMode

config = CodeCompressorConfig(
    preserve_imports=True,              # Always keep imports
    preserve_signatures=True,           # Always keep function signatures
    preserve_type_annotations=True,     # Keep type hints
    preserve_error_handlers=True,       # Keep try/except blocks
    preserve_decorators=True,           # Keep decorators
    docstring_mode=DocstringMode.FIRST_LINE,  # FULL, FIRST_LINE, REMOVE
    target_compression_rate=0.2,        # Keep 20% of tokens
    max_body_lines=5,                   # Lines to keep per function body
    min_tokens_for_compression=100,     # Skip small content
    language_hint=None,                 # Auto-detect if None
    fallback_to_llmlingua=True,         # Use LLMLingua for unknown langs
)

compressor = CodeAwareCompressor(config)
result = compressor.compress(code)
```

### Configuration Options

| Option | Default | Description |
|---|---|---|
| `preserve_imports` | `True` | Keep all import statements |
| `preserve_signatures` | `True` | Keep function/method signatures |
| `preserve_type_annotations` | `True` | Keep type hints |
| `preserve_error_handlers` | `True` | Keep try/except blocks |
| `preserve_decorators` | `True` | Keep decorators |
| `docstring_mode` | `FIRST_LINE` | How to handle docstrings: `FULL`, `FIRST_LINE`, `REMOVE` |
| `target_compression_rate` | `0.2` | Fraction of tokens to keep (0.2 = keep 20%) |
| `max_body_lines` | `5` | Max lines to keep per function body |
| `min_tokens_for_compression` | `100` | Skip files smaller than this |
| `language_hint` | `None` | Override language detection |
| `fallback_to_llmlingua` | `True` | Use LLMLingua for unsupported languages |

## Before and After

```python
# Before (full source file)
def process_data(items: List[str]) -> Dict[str, int]:
    """Process items and count occurrences."""
    result = {}
    for item in items:
        item = item.strip().lower()
        if item in result:
            result[item] += 1
        else:
            result[item] = 1
    return result

# After (signature preserved, body compressed)
def process_data(items: List[str]) -> Dict[str, int]:
    """Process items and count occurrences."""
    result = {}
    for item in items:
    # ... (5 lines compressed)
    pass
```

The LLM sees the function's purpose, its input/output types, and the general approach -- enough to reason about the code without needing every implementation line.

## Installation

```bash
# Install tree-sitter language pack
pip install "headroom-ai[code]"
```

## Memory Management

Tree-sitter parsers are lazy-loaded and cached. You can free memory when done:

```python
from headroom.transforms import is_tree_sitter_available, unload_tree_sitter

# Check if tree-sitter is installed
print(is_tree_sitter_available())  # True

# Free memory when done
unload_tree_sitter()
```

## Performance

| Metric | Value |
|---|---|
| Compression | 40-70% token reduction |
| Speed | ~10-50ms per file |
| Memory | ~50MB (tree-sitter parsers) |
| Syntax validity | Guaranteed |

<Callout type="info" title="Automatic routing">
  When you use the Headroom proxy or call `compress()`, source code is automatically detected and routed to CodeAwareCompressor. Direct usage gives you control over compression settings per language.
</Callout>