Serpen: Python Source Bundler

Serpen is a CLI and Python library that produces a single .py file from a multi-module Python project by inlining all first-party source files. This approach is inspired by JavaScript bundlers and aims to simplify deployment, especially in constrained environments like PySpark jobs, AWS Lambdas, and notebooks.

Features

• 🦀 Rust-based CLI using Ruff's Python AST parser
• 🐍 Python 3.10+ support
• 🌲 Tree-shaking logic to inline only the modules that are actually used
• 🔄 Smart circular dependency resolution with detailed diagnostics
• 🧹 Unused import trimming to clean up Python files standalone
• 📦 Requirements generation with optional requirements.txt output
• 🔧 Configurable import classification and source directories
• 🚀 Fast and memory-efficient

Installation

From PyPI (Python Package)

pip install serpen

From npm (Node.js CLI)

# Global installation
npm install -g serpen

# One-time use
npx serpen --help

🔐 Supply Chain Security: All npm packages include provenance attestations for enhanced security and verification.

Binary Downloads

Download pre-built binaries for your platform from the latest release:

• Linux x86_64: serpen_*_linux_x86_64.tar.gz
• Linux ARM64: serpen_*_linux_arm64.tar.gz
• macOS x86_64: serpen_*_darwin_x86_64.tar.gz
• macOS ARM64: serpen_*_darwin_arm64.tar.gz
• Windows x86_64: serpen_*_windows_x86_64.zip
• Windows ARM64: serpen_*_windows_arm64.zip

Each binary includes a SHA256 checksum file for verification.

Package Manager Installation

Aqua

If you use Aqua, add to your aqua.yaml:

registries:
  - type: standard
    ref: latest
packages:
  - name: tinovyatkin/serpen@latest

Then run:

aqua install

UBI (Universal Binary Installer)

Using UBI:

# Install latest version
ubi --project tinovyatkin/serpen

# Install specific version
ubi --project tinovyatkin/serpen --tag v0.4.1

# Install to specific directory
ubi --project tinovyatkin/serpen --in /usr/local/bin

From Source

git clone https://github.com/tinovyatkin/serpen.git
cd serpen
cargo build --release

Quick Start

Command Line Usage

# Basic bundling
serpen --entry src/main.py --output bundle.py

# Generate requirements.txt
serpen --entry src/main.py --output bundle.py --emit-requirements

# Verbose output
serpen --entry src/main.py --output bundle.py --verbose

# Custom config file
serpen --entry src/main.py --output bundle.py --config my-serpen.toml

Configuration

Serpen supports hierarchical configuration with the following precedence (highest to lowest):

1. CLI-provided config (--config flag)
2. Environment variables (with SERPEN_ prefix)
3. Project config (serpen.toml in current directory)
4. User config (~/.config/serpen/serpen.toml)
5. System config (/etc/serpen/serpen.toml on Unix, %SYSTEMDRIVE%\ProgramData\serpen\serpen.toml on Windows)
6. Default values

Configuration File Format

Create a serpen.toml file:

# Source directories to scan for first-party modules
src = ["src", ".", "lib"]

# Known first-party module names
known_first_party = [
    "my_internal_package",
]

# Known third-party module names
known_third_party = [
    "requests",
    "numpy",
    "pandas",
]

# Whether to preserve comments in the bundled output
preserve_comments = true

# Whether to preserve type hints in the bundled output
preserve_type_hints = true

# Target Python version for standard library checks
# Supported: "py38", "py39", "py310", "py311", "py312", "py313"
target-version = "py310"

Environment Variables

All configuration options can be overridden using environment variables with the SERPEN_ prefix:

# Comma-separated lists
export SERPEN_SRC="src,lib,custom_dir"
export SERPEN_KNOWN_FIRST_PARTY="mypackage,myotherpackage"
export SERPEN_KNOWN_THIRD_PARTY="requests,numpy"

# Boolean values (true/false, 1/0, yes/no, on/off)
export SERPEN_PRESERVE_COMMENTS="false"
export SERPEN_PRESERVE_TYPE_HINTS="true"

# String values
export SERPEN_TARGET_VERSION="py312"

Configuration Locations

• Project: ./serpen.toml
• User:
  • Linux/macOS: ~/.config/serpen/serpen.toml
  • Windows: %APPDATA%\serpen\serpen.toml
• System:
  • Linux/macOS: /etc/serpen/serpen.toml or /etc/xdg/serpen/serpen.toml
  • Windows: %SYSTEMDRIVE%\ProgramData\serpen\serpen.toml

How It Works

1. Module Discovery: Scans configured source directories to discover first-party Python modules
2. Import Classification: Classifies imports as first-party, third-party, or standard library
3. Dependency Graph: Builds a dependency graph and performs topological sorting
4. Circular Dependency Resolution: Detects and intelligently resolves function-level circular imports
5. Tree Shaking: Only includes modules that are actually imported (directly or transitively)
6. Code Generation: Generates a single Python file with proper module separation
7. Requirements: Optionally generates requirements.txt with third-party dependencies

Output Structure

The bundled output follows this structure:

#!/usr/bin/env python3
# Generated by Serpen - Python Source Bundler

# Preserved imports (stdlib and third-party)
import os
import sys
import requests

# ─ Module: utils/helpers.py ─
def greet(name: str) -> str:
    return f"Hello, {name}!"

# ─ Module: models/user.py ─
class User:
    def **init**(self, name: str):
        self.name = name

# ─ Entry Module: main.py ─
from utils.helpers import greet
from models.user import User

def main():
    user = User("Alice")
    print(greet(user.name))

if **name** == "**main**":
    main()

Use Cases

PySpark Jobs

Deploy complex PySpark applications as a single file:

serpen --entry spark_job.py --output dist/spark_job_bundle.py --emit-requirements
spark-submit dist/spark_job_bundle.py

AWS Lambda

Package Python Lambda functions with all dependencies:

serpen --entry lambda_handler.py --output deployment/handler.py
# Upload handler.py + requirements.txt to Lambda

Special Considerations

Pydantic Compatibility

Serpen preserves class identity and module structure to ensure Pydantic models work correctly:

# Original: models/user.py
class User(BaseModel):
    name: str

# Bundled output preserves **module** and class structure

Pandera Decorators

Function and class decorators are preserved with their original module context:

# Original: validators/schemas.py
@pa.check_types
def validate_dataframe(df: DataFrame[UserSchema]) -> DataFrame[UserSchema]:
    return df

# Bundled output maintains decorator functionality

Circular Dependencies

Serpen intelligently handles circular dependencies with advanced detection and resolution:

Resolvable Cycles (Function-Level)

Function-level circular imports are automatically resolved and bundled successfully:

# module_a.py
from module_b import process_b
def process_a(): return process_b() + "->A"

# module_b.py  
from module_a import get_value_a
def process_b(): return f"B(using_{get_value_a()})"

Result: ✅ Bundles successfully with warning log

Unresolvable Cycles (Module Constants)

Temporal paradox patterns are detected and reported with detailed diagnostics:

# constants_a.py
from constants_b import B_VALUE
A_VALUE = B_VALUE + 1  # ❌ Unresolvable

# constants_b.py
from constants_a import A_VALUE  
B_VALUE = A_VALUE * 2  # ❌ Temporal paradox

Result: ❌ Fails with detailed error message and resolution suggestions:

Unresolvable circular dependencies detected:

Cycle 1: constants_b → constants_a
  Type: ModuleConstants
  Reason: Module-level constant dependencies create temporal paradox - cannot be resolved through bundling

Comparison with Other Tools

Development

Building from Source

git clone https://github.com/tinovyatkin/serpen.git
cd serpen

# Build Rust CLI
cargo build --release

# Build Python package
pip install maturin
maturin develop

# Run tests
cargo test

Project Structure

serpen/
├── src/                    # Rust source code
│   ├── main.rs            # CLI entry point
│   ├── bundler.rs         # Core bundling logic
│   ├── resolver.rs        # Import resolution
│   ├── emit.rs            # Code generation
│   └── ...
├── python/serpen/         # Python package
├── tests/                 # Test suites
│   └── fixtures/          # Test projects
├── docs/                  # Documentation
└── Cargo.toml            # Rust dependencies

Contributing

Development Setup

# Clone the repository
git clone https://github.com/tinovyatkin/serpen.git
cd serpen

# Install Rust toolchain and components
rustup component add llvm-tools-preview
cargo install cargo-llvm-cov

# Build Rust CLI
cargo build --release

# Build Python package
pip install maturin
maturin develop

# Run tests
cargo test

Code Coverage

The project uses cargo-llvm-cov for code coverage analysis:

# Generate text coverage report (Istanbul-style)
cargo coverage-text

# Generate HTML coverage report and open in browser
cargo coverage

# Generate LCOV format for CI
cargo coverage-lcov

# Clean coverage data
cargo coverage-clean

Branch Coverage (Experimental):

# Requires nightly Rust for branch coverage
cargo +nightly coverage-branch

Coverage reports are automatically generated in CI and uploaded to Codecov. See docs/coverage.md for detailed coverage documentation.

Note: If you see zeros in the "Branch Coverage" column in HTML reports, this is expected with stable Rust. Branch coverage requires nightly Rust and is experimental.

Contributing Guidelines

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

This project uses a dual licensing approach:

• Source Code: Licensed under the MIT License
• Documentation: Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0)

What this means:

• For the source code: You can freely use, modify, and distribute the code for any purpose with minimal restrictions under the MIT license.
• For the documentation: You can share, adapt, and use the documentation for any purpose (including commercially) as long as you provide appropriate attribution under CC BY 4.0.

See the LICENSE file for the MIT license text and docs/LICENSE for the CC BY 4.0 license text.

Acknowledgments

• Ruff: Python AST parsing and import resolution logic inspiration
• Maturin: Python-Rust integration

Roadmap

• Smart circular dependency resolution - ✅ Completed in v0.4.4+
• Source maps for debugging
• Parallel processing
• Package flattening mode
• Comment and type hint stripping
• Plugin system for custom transformations

For more examples and detailed documentation, visit our documentation site.

For detailed documentation on the unused import trimmer, see docs/unused_import_trimmer.md.