spartastruct 0.2.3

Instant architecture diagrams for any Python, JavaScript, or TypeScript project

Point SpartaStruct at any Python, JavaScript, or TypeScript project and get 11 architecture diagrams in under a minute.
PDF and PNG export. Works offline. Optionally enriched by any LLM.

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⚡ Quick Start

Estimated time: 2 minutes

Step 1 — Install the dependencies

# Install SpartaStruct
pip install spartastruct

# Install Node.js (https://nodejs.org) — that's it.
# SpartaStruct uses npx to run the Mermaid CLI automatically.

Step 2 — Run it on your project

spartastruct analyze /path/to/your/project --no-llm

That's it. Eleven PDF files appear in a spartadocs/ folder inside your project.

your-project/
└── spartadocs/
    ├── class_diagram.pdf      ← all your classes and how they relate
    ├── er_diagram.pdf         ← database tables and relationships
    ├── dfd.pdf                ← HTTP routes → services → database
    ├── flowchart.pdf          ← app logic and entry points
    ├── function_graph.pdf     ← which functions call which
    ├── module_graph.pdf       ← which files import which
    ├── sequence_diagram.pdf   ← how components interact at runtime
    ├── state_diagram.pdf      ← state machines and transitions
    ├── api_map.pdf            ← all HTTP routes grouped by resource
    ├── component_map.pdf      ← service layers and dependencies
    └── event_flow.pdf         ← async tasks and event/message flow

Step 3 — Add LLM enrichment (optional)

spartastruct init                                   # creates ~/.spartastruct/config.toml
spartastruct config --api-key anthropic YOUR_KEY    # store your API key
spartastruct analyze /path/to/your/project          # runs with LLM enrichment

The LLM improves diagram labels, adds descriptions, and connects the dots between related components.

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How It Works

SpartaStruct works in four stages:

1. Walk — finds every source file in your project, skipping node_modules, venv, __pycache__, build artifacts, etc.
2. Analyze — parses each file statically (no code is executed). Extracts classes, functions, routes, database models, imports, and call relationships.
3. Enrich (optional) — sends the analysis to an LLM, which improves the diagram and adds a plain-English description.
4. Export — converts each Mermaid diagram to a PDF or PNG using the Mermaid CLI, invoked automatically via npx (no separate install needed).

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The 11 Diagrams Explained

Each diagram answers a different question about your codebase.

📦 Class Diagram — "What classes exist and how do they relate?"

Shows every class in your project, its attributes (variables), its methods (functions), and whether it inherits from another class.

classDiagram
    class UserService {
        +db: Database
        +get_user(id) User
        +create_user(data) User
    }
    class AdminService {
        +promote(user_id)
    }
    AdminService --|> UserService : extends

Useful for: Understanding the shape of your code, onboarding new developers, spotting classes that do too much.

🗄️ ER Diagram — "What does the database look like?"

Shows your database tables (ORM models) and the relationships between them — one-to-many, many-to-many, foreign keys, etc.

Supports: SQLAlchemy, Django ORM, Tortoise ORM, Peewee, TypeORM, Sequelize, Mongoose, Prisma.

erDiagram
    User {
        int id PK
        string email
        string name
    }
    Order {
        int id PK
        int user_id FK
        float total
    }
    User ||--o{ Order : "places"

Useful for: Database design reviews, writing migrations, explaining the data model to non-engineers.

🌊 Data Flow Diagram (DFD) — "How does data move through the app?"

Traces the path from an HTTP request → controller/handler → service layer → database. Particularly useful for API-heavy projects.

Supports: FastAPI, Flask, Django, Express, NestJS.

Useful for: Security reviews, debugging unexpected behaviour, understanding which routes hit which tables.

🔄 Flowchart — "What does the app actually do step by step?"

A top-down flow diagram starting from your entry points (main(), run(), CLI handlers, index files, etc.) showing the sequence of processing.

Useful for: Explaining app logic to stakeholders, spotting dead code, documenting workflows.

🕸️ Function Graph — "Which functions call which?"

A left-to-right call graph grouping functions by file. Async functions are highlighted in blue. Entry-point functions are highlighted in yellow.

graph LR
    subgraph main["main.ts"]
        fn0["async handleRequest()"]:::async
        fn1["validate()"]
        fn2["save()"]
        fn0 --> fn1
        fn0 --> fn2
    end
    classDef async fill:#e8f4fd,stroke:#2196f3

Edges are automatically deduplicated and capped at 8 per node to keep the diagram readable.

Useful for: Finding tightly-coupled functions, understanding call depth, refactoring planning.

🗺️ Module Graph — "Which files import which?"

A top-down dependency graph of your project's files. Local imports (your own code) are shown with solid lines. Third-party imports are shown with dashed lines.

Useful for: Identifying circular imports, understanding coupling between modules, planning a refactor.

🔀 Sequence Diagram — "How do components interact at runtime?"

Traces the call sequence from an HTTP request through route handlers, service methods, repository calls, and database queries. Shows participant lifelines and message arrows.

Useful for: Code reviews, debugging request flows, onboarding new developers to understand runtime behaviour.

🔄 State Diagram — "What states can this object be in?"

Detects classes with status, state, or stage attributes and methods that trigger transitions (approve, cancel, complete, etc.). Renders a nested state machine per class, falling back to a generic request lifecycle.

Useful for: Understanding order/payment/workflow states, designing new state machines, reviewing business logic.

🗂️ API Endpoint Map — "What routes does this app expose?"

Groups all HTTP routes by resource (first path segment). Each route shows its method, path, and handler name. Methods are colour-coded: GET (green), POST (blue), PUT/PATCH (yellow), DELETE (red).

Useful for: API reviews, writing API documentation, spotting missing or duplicate endpoints.

🏗️ Component Map — "What are the logical layers of this app?"

Groups classes by naming convention into Controllers, Services, Repositories, Models, and Utils layers. Draws dependency arrows between layers and shows detected external frameworks.

Useful for: Architecture reviews, onboarding, identifying layering violations (e.g. a Controller directly accessing a Repository).

📨 Event & Message Flow — "How does async messaging work?"

Detects Celery tasks (@shared_task, @app.task), event emitters (emit(), publish(), dispatch()), and async functions. Shows producers, consumers, and the message broker/bus between them.

Useful for: Understanding background job pipelines, debugging message flow, reviewing Celery task architecture.

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Full CLI Reference

spartastruct analyze [PATH]

Analyzes a project and writes PDFs.

spartastruct analyze .                          # analyze current directory
spartastruct analyze /path/to/project           # analyze a specific path
spartastruct analyze . --no-llm                 # skip LLM (fast, offline)
spartastruct analyze . --model openai/gpt-4o    # use a different LLM
spartastruct analyze . --output ./my-docs       # write PDFs to a custom folder
spartastruct analyze . --format png             # export as PNG (transparent, 3× scale)
spartastruct analyze . --format both            # export PDF and PNG

Flag	Default	What it does
--no-llm	off	Skip LLM enrichment entirely. Static diagrams only. Fully offline.
--model MODEL	from config	Use a different LLM model just for this run. Uses litellm format: provider/model.
--output DIR	spartadocs	Write PDFs/PNGs here instead of the default spartadocs/ folder.
--format FORMAT	pdf	Output format: pdf, png (transparent background, 3× scale), or both.

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spartastruct init

Creates the config file at ~/.spartastruct/config.toml with default settings. Run this once before using LLM features.

spartastruct init

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spartastruct config

View or update your settings.

spartastruct config --show                              # print current settings
spartastruct config --model anthropic/claude-opus-4-7  # change the default model
spartastruct config --output-dir ./architecture-docs    # change the default output folder
spartastruct config --api-key anthropic sk-ant-...      # save an API key
spartastruct config --api-key openai sk-...             # save multiple keys

Flag	What it does
--show	Print your current config. Use this to verify settings.
--model MODEL	Set the default LLM model. Accepts any litellm-format string.
--output-dir DIR	Set where PDFs are saved by default.
--api-key PROVIDER KEY	Save an API key for a provider. Provider name must match the litellm prefix.

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LLM Setup

SpartaStruct uses litellm under the hood, which means it works with almost any LLM provider.

Supported Providers

Provider	Config key	Model format
Anthropic	anthropic	anthropic/claude-haiku-4-5-20251001
OpenAI	openai	openai/gpt-4o
Google Gemini	gemini	gemini/gemini-2.0-flash
Groq	groq	groq/llama-3.1-70b-versatile
Mistral	mistral	mistral/mistral-large-latest
Cohere	cohere	cohere/command-r-plus
Together AI	together	together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
Ollama (local)	ollama	ollama/llama3.2

Default model: anthropic/claude-haiku-4-5-20251001 (fast and cheap)

Setting Up Anthropic (example)

# 1. Get your API key from https://console.anthropic.com
# 2. Store it
spartastruct config --api-key anthropic sk-ant-...
# 3. Verify
spartastruct config --show
# 4. Run
spartastruct analyze /path/to/project

Using Ollama (fully local, no API key needed)

# Install Ollama from https://ollama.com, then pull a model
ollama pull llama3.2

# Tell SpartaStruct to use it
spartastruct config --model ollama/llama3.2

# Run — no internet connection needed
spartastruct analyze /path/to/project

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Supported Languages

SpartaStruct auto-detects your project's primary language and picks the right analyzer. For polyglot projects (e.g. an API backend with a TypeScript frontend in the same repo), both analyzers run and results are merged into a single set of diagrams.

Language	Extensions	Analyzer
Python	.py	AST-based — classes, functions, routes, ORM models, imports
JavaScript	.js, .jsx	Regex-based — classes, functions, Express routes, imports
TypeScript	.ts, .tsx	Regex-based — classes, interfaces, functions, NestJS routes, imports

Detected JS/TS frameworks: Express, NestJS, Next.js, React, Vue, Angular, Nuxt, TypeORM, Sequelize, Mongoose, Prisma, GraphQL, Apollo, Socket.IO, Axios, Jest, Vitest, RxJS

How auto-detection works: SpartaStruct counts source files by language. Single-language projects use the matching analyzer. Mixed projects run both and merge the results.

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Supported Frameworks

SpartaStruct auto-detects which frameworks your project uses and includes that in the analysis.

Category	Frameworks
Web (Python)	FastAPI, Flask, Django
Web (JS/TS)	Express, NestJS, Next.js
Database / ORM	SQLAlchemy, Django ORM, Tortoise ORM, Peewee, Alembic, TypeORM, Sequelize, Mongoose, Prisma
Task Queues	Celery
Validation	Pydantic
Testing	Pytest, Jest, Vitest
HTTP Clients	Requests, HTTPX, Axios
Data Science	NumPy, Pandas, PyTorch, TensorFlow
Frontend	React, Vue, Angular, Nuxt

Detection is automatic — you don't need to tell SpartaStruct which frameworks you use.

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Requirements

Tool	Version	Install
Python	3.10+	python.org
Node.js	18+	nodejs.org

Node.js is only needed for PDF/PNG export. SpartaStruct uses npx to run the Mermaid CLI automatically — no separate install step required. The analysis and diagram generation work without Node.js at all.

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Config File Reference

The config lives at ~/.spartastruct/config.toml. You can edit it directly or use spartastruct config.

model = "anthropic/claude-haiku-4-5-20251001"
output_dir = "spartadocs"

[api_keys]
anthropic = "sk-ant-..."
openai = "sk-..."

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Project Structure

spartastruct/
├── analyzer/
│   ├── base.py              # data types: FileResult, ClassInfo, FunctionInfo, etc.
│   ├── python_analyzer.py   # AST-based analyzer for Python
│   └── js_analyzer.py       # regex-based analyzer for JavaScript / TypeScript
├── diagrams/
│   ├── class_diagram.py     # classDiagram generator
│   ├── er_diagram.py        # erDiagram generator
│   ├── dfd.py               # data flow diagram generator
│   ├── flowchart.py         # flowchart generator
│   ├── function_graph.py    # function call graph generator
│   ├── module_graph.py      # module dependency graph generator
│   ├── sequence_diagram.py  # sequenceDiagram generator
│   ├── state_diagram.py     # stateDiagram-v2 generator
│   ├── api_map.py           # API endpoint map generator
│   ├── component_map.py     # component / service layer map generator
│   └── event_flow.py        # event & message flow generator
├── llm/
│   ├── client.py            # litellm wrapper, failure tracking, retry logic
│   └── prompts.py           # system prompts per diagram type
├── renderer/
│   ├── markdown_renderer.py # assembles sections from diagram results
│   └── pdf_exporter.py      # calls mmdc to convert Mermaid → PDF/PNG
├── utils/
│   ├── file_walker.py       # finds source files, respects ignore patterns
│   └── framework_detector.py# detects frameworks from imports
├── templates/
│   └── structure.md.j2      # Jinja2 template for markdown layout
├── config.py                # TOML config load/save
└── cli.py                   # Click CLI (analyze, init, config)

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Contributing

git clone https://github.com/yashrandive11/spartastruct
cd spartastruct
pip install -e ".[dev]"
pytest

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_{Built with Python · Mermaid.js · litellm · Rich · Click}