Building Javascript-pathway Package: A TypeScript/JavaScript Tool

Oct 30, 2025 by Admin 66 views

Building the javascript-pathway Package: TypeScript/JavaScript Analysis Tool

Hey guys! Today, we're diving deep into the creation of a brand-new package called javascript-pathway. This tool is designed to analyze JavaScript and TypeScript codebases, and it's going to be a game-changer for AI agents working with these languages. Think of it as a super-smart assistant that can understand and dissect your code, making it easier to identify patterns, potential issues, and much more. So, let's get started and explore what this package is all about!

Objective

The primary objective here is to create a javascript-pathway package that can analyze JavaScript and TypeScript code. This package will integrate with pnpm package manager primitives, enabling AI agents to effectively work with JavaScript/TypeScript projects. We aim to provide a robust set of tools that can parse code, detect patterns, and offer valuable insights into the codebase. This is crucial for automating code analysis and improving overall project quality. It’s like giving AI agents a pair of glasses to see the code more clearly.

Background

Following the successful model of python-pathway, this new package will offer several key features. Just like its Python counterpart, javascript-pathway will provide AST parsing for JavaScript/TypeScript code, allowing us to break down the code into a structured tree-like representation. It will also include pattern detection capabilities, helping to identify both useful patterns and potential anti-patterns in the code. Furthermore, the package will feature integration with pnpm primitives for dependency analysis, giving us a clear view of how different parts of the project rely on each other. Finally, it will offer wrapper primitives for workflow composition, making it easier to integrate these analysis tools into larger automated processes. Keep in mind, guys, that this package will be implemented in Python, but it will analyze JavaScript/TypeScript code, leveraging the best of both worlds.

Scope

1. Package Structure

The package will be structured as follows:

packages/javascript-pathway/
├── src/javascript_pathway/
│   ├── __init__.py
│   ├── analyzer.py          # JS/TS AST parsing and code analysis
│   ├── detector.py          # Pattern detection
│   ├── models.py            # Pydantic models for analysis results
│   ├── primitives.py        # Wrapper primitives for workflows
│   └── utils.py             # Utility functions
├── tests/
│   ├── __init__.py
│   ├── test_analyzer.py
│   ├── test_detector.py
│   ├── test_primitives.py
│   └── fixtures/
│       ├── sample_code.js
│       ├── sample_code.ts
│       └── sample_package.json
├── examples/
│   ├── basic_analysis.py
│   └── workflow_integration.py
├── pyproject.toml
└── README.md

This structure is designed to keep things organized and maintainable. The src/javascript_pathway/ directory will house the core logic, including modules for analysis, pattern detection, data models, and workflow primitives. The tests/ directory will contain all the tests, ensuring our code works as expected. The examples/ directory will provide practical usage examples, helping users understand how to use the package. This is all about creating a clear and easy-to-navigate package structure, guys.

2. Core Components

2.1 JavaScript/TypeScript Analyzer (`analyzer.py`)

The JavaScript/TypeScript Analyzer, found in analyzer.py, is a critical component of the javascript-pathway package. Its primary functionality includes parsing JavaScript/TypeScript source code into an Abstract Syntax Tree (AST). Think of the AST as a detailed map of the code's structure, making it easier to analyze. The analyzer will also extract key information, such as classes and their methods, functions and their signatures, imports/exports (covering both ES modules and CommonJS), React components (if applicable), and TypeScript types and interfaces. This comprehensive approach ensures that we capture all the essential elements of the code. Support for modern JavaScript (ES2023+) and TypeScript syntax is also a top priority, ensuring the tool remains relevant and effective as the languages evolve. We need to make sure that the analyzer can handle the latest and greatest features of JavaScript and TypeScript.

The parsing strategy involves using a Python library to parse JavaScript/TypeScript, such as esprima via subprocess, or the more robust tree-sitter. Another alternative is to call a Node.js-based parser from Python, which can be particularly useful for handling complex TypeScript syntax. The choice of parsing strategy will depend on factors such as performance, accuracy, and ease of integration. We want to strike the right balance between these considerations. Here's an example of how the API might look:

from javascript_pathway import JavaScriptAnalyzer

analyzer = JavaScriptAnalyzer()
result = analyzer.analyze_file("path/to/file.ts")

print(result.classes)      # List of ClassInfo objects
print(result.functions)    # List of FunctionInfo objects
print(result.imports)      # List of ImportInfo objects

This API allows users to easily analyze a file and access the extracted information. It’s all about making the tool as user-friendly as possible, guys!

2.2 Pattern Detector (`detector.py`)

The Pattern Detector, located in detector.py, is another vital part of the package. Its main functionality is to detect common JavaScript/TypeScript patterns. This includes recognizing patterns like React hooks usage, async/await patterns, Promise chains, module patterns (both ES modules and CommonJS), and TypeScript utility types. Identifying these patterns helps in understanding the code's structure and behavior. Additionally, the detector will identify anti-patterns, such as callback hell, missing error handling, unused imports, missing TypeScript types, and even console.log statements in production code. Spotting these anti-patterns can significantly improve code quality and maintainability. It’s like having a built-in code reviewer that never gets tired!

Here’s an example of how the Pattern Detector API might look:

from javascript_pathway import PatternDetector

detector = PatternDetector()
patterns = detector.detect_patterns("path/to/file.ts")

for pattern in patterns:
    print(f"{pattern.name}: {pattern.location}")

This API makes it straightforward to detect patterns in a given file and get information about their location. This is super helpful for quickly identifying areas of interest or potential concern in the code.

2.3 Pydantic Models (`models.py`)

Pydantic models, defined in models.py, play a crucial role in structuring the data produced by the analyzer and detector. These models ensure that the data is consistent and easy to work with. The data models will include classes such as ClassInfo, FunctionInfo, ImportInfo, and AnalysisResult. Let's take a closer look at what these models might look like:

from pydantic import BaseModel

class ClassInfo(BaseModel):
    name: str
    extends: str | None
    methods: list[str]
    properties: list[str]
    is_react_component: bool
    line_number: int

class FunctionInfo(BaseModel):
    name: str
    parameters: list[str]
    return_type: str | None
    is_async: bool
    is_arrow_function: bool
    line_number: int

class ImportInfo(BaseModel):
    source: str
    imports: list[str]
    is_default: bool
    is_namespace: bool

class AnalysisResult(BaseModel):
    file_path: str
    language: str  # "javascript" or "typescript"
    classes: list[ClassInfo]
    functions: list[FunctionInfo]
    imports: list[ImportInfo]
    exports: list[str]
    total_lines: int
    complexity_score: float

These models provide a clear and structured way to represent the analysis results. For example, ClassInfo contains information about a class, such as its name, the class it extends, its methods and properties, whether it’s a React component, and the line number where it’s defined. Similarly, FunctionInfo contains details about a function, including its name, parameters, return type, whether it’s async, if it’s an arrow function, and its line number. AnalysisResult ties it all together, providing a comprehensive view of the analyzed file. It’s all about making the data accessible and understandable, guys.

2.4 Workflow Primitives (`primitives.py`)

Workflow primitives, housed in primitives.py, are essential for integrating javascript-pathway into larger workflows, especially within the TTA.dev ecosystem. These primitives provide a way to compose analysis tasks with other operations, making it easier to automate complex processes. Integration with TTA.dev is a key goal here. Here’s an example of how a workflow primitive might look:

from tta_dev_primitives import WorkflowPrimitive, WorkflowContext
from javascript_pathway.models import AnalysisResult

class JSCodeAnalysisPrimitive(WorkflowPrimitive[str, AnalysisResult]):
    """Analyze JavaScript/TypeScript code and return structured results"""
    
    async def execute(self, file_path: str, context: WorkflowContext) -> AnalysisResult:
        analyzer = JavaScriptAnalyzer()
        return analyzer.analyze_file(file_path)

class PackageJsonAnalysisPrimitive(WorkflowPrimitive[str, dict]):
    """Analyze package.json dependencies using pnpm"""
    
    async def execute(self, project_path: str, context: WorkflowContext) -> dict:
        # Use PnpmListPrimitive to get dependency tree
        # Parse and analyze dependencies
        pass

In this example, we define two primitives: JSCodeAnalysisPrimitive for analyzing JavaScript/TypeScript code and PackageJsonAnalysisPrimitive for analyzing package.json dependencies using pnpm. These primitives extend WorkflowPrimitive from tta_dev_primitives, allowing them to be easily composed into workflows. It’s all about making the analysis process modular and reusable.

3. Integration with `pnpm` Primitives

Integrating with pnpm primitives is crucial for dependency analysis. This allows us to understand how different parts of a project depend on each other, which is vital for tasks like refactoring and identifying potential conflicts. Dependency analysis will be achieved by composing javascript-pathway primitives with pnpm primitives. Here’s an example of how this might look:

from tta_dev_primitives.package_managers import PnpmListPrimitive
from javascript_pathway import PackageJsonAnalysisPrimitive

# Compose workflow
workflow = (
    PnpmListPrimitive() >>
    PackageJsonAnalysisPrimitive()
)

result = await workflow.execute(project_path, context)

In this example, we compose PnpmListPrimitive (which lists the dependencies) with PackageJsonAnalysisPrimitive (which analyzes the package.json file). This creates a workflow that can analyze the dependencies of a project. It’s all about leveraging the power of pnpm to gain deeper insights into the project structure.

4. Parsing Strategy Options

Choosing the right parsing strategy is crucial for the performance and accuracy of javascript-pathway. We have several options to consider:

Option 1: Tree-sitter (Recommended)

Use tree-sitter Python bindings
Supports JavaScript and TypeScript
Fast and reliable
Dependency: tree-sitter, tree-sitter-javascript, tree-sitter-typescript

Option 2: Subprocess to Node.js Parser

Call Node.js-based parser (e.g., @babel/parser, typescript compiler)
More accurate for complex TypeScript
Requires Node.js runtime

Option 3: Esprima via PyExecJS

Use PyExecJS to run Esprima in Python
Limited TypeScript support
Simpler setup

Our recommendation is to start with Tree-sitter due to its performance and reliability. Tree-sitter is a parser generator tool and library that can build a fast and robust parser for JavaScript and TypeScript. It provides excellent performance and supports both languages well. However, we might also explore the option of using a Node.js-based parser for more complex TypeScript scenarios. It’s all about choosing the right tool for the job, guys.

5. Testing Strategy

A robust testing strategy is essential to ensure the quality and reliability of javascript-pathway. Our test coverage will include:

✅ Unit tests for JavaScript AST parsing
✅ Unit tests for TypeScript AST parsing
✅ Unit tests for pattern detection
✅ Integration tests with pnpm primitives
✅ End-to-end workflow tests
✅ 100% code coverage required

We aim for 100% code coverage to ensure that every part of the package is thoroughly tested. This includes unit tests for individual components, integration tests for how components work together, and end-to-end tests for complete workflows. We’ll also use test fixtures, including sample JavaScript files (ES modules, CommonJS), sample TypeScript files (with types, interfaces), sample React components, sample package.json files, and mock pnpm command outputs. It’s all about building a solid foundation of tests to catch any issues early.

Dependencies

Our project has a few dependencies to keep in mind:

Blocked by:

#TBD - Implement Package Manager Primitives for Python (uv) and JavaScript (pnpm)

Python Dependencies:

tree-sitter>=0.20.0 - AST parsing
tree-sitter-javascript - JavaScript grammar
tree-sitter-typescript - TypeScript grammar
pydantic>=2.0 - Data models
tta-dev-primitives - Workflow integration

Optional Dependencies:

PyExecJS - Alternative JavaScript execution

We need to ensure that these dependencies are properly managed to avoid any compatibility issues. The tree-sitter library and its JavaScript and TypeScript grammars are crucial for parsing, while pydantic helps us define our data models, and tta-dev-primitives is essential for workflow integration. It’s all about managing our tools effectively, guys.

Implementation Plan

We have a phased implementation plan to ensure we build javascript-pathway in a structured way:

Phase 1: Package Setup (Week 1)

✅ Create pyproject.toml with package metadata
✅ Add to root workspace members
✅ Create directory structure
✅ Write README.md
✅ Set up Tree-sitter parsers

Phase 2: Core Analysis (Week 1-2)

✅ Implement JavaScriptAnalyzer with Tree-sitter
✅ Add TypeScript support
✅ Implement PatternDetector
✅ Create Pydantic models
✅ Write unit tests

Phase 3: Primitive Integration (Week 2)

✅ Implement JSCodeAnalysisPrimitive
✅ Implement PackageJsonAnalysisPrimitive
✅ Integrate with pnpm primitives
✅ Add observability

Phase 4: Testing & Documentation (Week 3)

✅ Write comprehensive test suite
✅ Create usage examples
✅ Document integration patterns
✅ Update AGENTS.md

This plan breaks down the development process into manageable phases, ensuring we stay on track and deliver a high-quality package. It’s all about having a clear roadmap, guys.

Deliverables

Our deliverables for this project include:

[ ] Working javascript-pathway package with JavaScript/TypeScript parsing
[ ] Pattern detection for common JavaScript/TypeScript patterns
[ ] Integration with pnpm primitives for dependency analysis
[ ] Pydantic v2 models for all analysis results
[ ] Workflow primitives for composition
[ ] 100% test coverage
[ ] Comprehensive documentation and examples
[ ] CI/CD pipeline integration

These deliverables represent the tangible outcomes of our work, ensuring that we deliver a complete and functional package. It’s all about setting clear goals and achieving them.

Acceptance Criteria

To ensure that javascript-pathway meets our standards, we have defined specific acceptance criteria:

[ ] Package is registered in root pyproject.toml workspace members
[ ] JavaScript parsing works for ES2023+ syntax
[ ] TypeScript parsing works with types and interfaces
[ ] Pattern detection identifies at least 5 common patterns
[ ] Integration with pnpm primitives is functional
[ ] All primitives extend WorkflowPrimitive[T, U]
[ ] Test coverage is 100%
[ ] Documentation includes usage examples
[ ] CI/CD pipeline validates the package

These criteria provide a clear checklist for evaluating the package, ensuring that it meets all the necessary requirements. It’s all about setting the bar high and making sure we clear it, guys.

Example Usage

Let's look at some examples of how javascript-pathway can be used:

Basic Analysis

from javascript_pathway import JavaScriptAnalyzer

analyzer = JavaScriptAnalyzer()
result = analyzer.analyze_file("src/components/MyComponent.tsx")

print(f"Found {len(result.classes)} classes")
print(f"Found {len(result.functions)} functions")
print(f"Is React component: {result.classes[0].is_react_component}")

This example shows how to analyze a file and extract information about classes and functions. It’s a simple yet powerful way to get insights into your code.

Workflow Integration

from tta_dev_primitives import SequentialPrimitive
from javascript_pathway import JSCodeAnalysisPrimitive
from tta_dev_primitives.package_managers import PnpmInstallPrimitive

# Analyze code after installing dependencies
workflow = (
    PnpmInstallPrimitive(frozen_lockfile=True) >>
    JSCodeAnalysisPrimitive()
)

result = await workflow.execute("src/index.ts", context)

This example demonstrates how to integrate javascript-pathway into a larger workflow, analyzing code after installing dependencies. It’s all about making the analysis process seamless and automated.

Related Issues

We have some related issues to keep in mind:

#TBD - Implement Package Manager Primitives for Python (uv) and JavaScript (pnpm)
#TBD - Populate python-pathway Package with Code Analysis Utilities

These issues highlight areas where we need to coordinate our efforts to ensure everything works smoothly. It’s all about staying connected and working together, guys.

References

Here are some useful references for this project:

Tree-sitter: https://tree-sitter.github.io/tree-sitter/
Tree-sitter JavaScript: https://github.com/tree-sitter/tree-sitter-javascript
Tree-sitter TypeScript: https://github.com/tree-sitter/tree-sitter-typescript
TTA.dev Primitives: PRIMITIVES_CATALOG.md
Architecture: docs/architecture/COMPONENT_INTEGRATION_ANALYSIS.md

These references provide valuable information and context for the project, helping us make informed decisions and build a robust package. It’s all about doing our homework, guys!

So, there you have it! A comprehensive overview of the javascript-pathway package. We're super excited about this project and the potential it has to revolutionize how we analyze JavaScript and TypeScript code. Stay tuned for more updates as we progress, and feel free to dive in and contribute. Let's build something awesome together!