https://github.com/mkh-user/graphite
A clean, embedded graph database engine for Python.
https://github.com/mkh-user/graphite
graphdb pypi-package python
Last synced: 24 days ago
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A clean, embedded graph database engine for Python.
- Host: GitHub
- URL: https://github.com/mkh-user/graphite
- Owner: mkh-user
- License: mit
- Created: 2026-01-29T10:10:17.000Z (about 1 month ago)
- Default Branch: Main
- Last Pushed: 2026-01-30T10:36:20.000Z (29 days ago)
- Last Synced: 2026-02-01T21:33:33.860Z (27 days ago)
- Topics: graphdb, pypi-package, python
- Language: Python
- Homepage: https://pypi.org/project/graphitedb
- Size: 42 KB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Graphite
A clean, embedded graph database engine for Python.
---
**Graphite** is a lightweight yet flexible **graph database engine** implemented in pure Python.
It is designed to model graph-like data inside large Python codebases **without introducing the complexity of an external database**.
---
## Features
### 🧩 Embedded by Design
Graphite is not a separate service or infrastructure dependency.
It lives inside your project, evolves with it, and collaborates naturally with your existing code.
No servers. No ports. No deployment headaches.
---
### 🛠 Ready-made, Customizable Module
Graphite is intentionally simple and hackable.
You can fork it, modify it, or deeply integrate it into your project without fighting rigid abstractions.
The database adapts to your project — not the other way around.
---
### 🐍 Native Python API
Everything is done through Python APIs.
No query strings.
DSL parsing is just an optional layer.
No context switching.
Your editor already knows how to autocomplete and document your queries.
---
### 🔍 Query? It’s Code.
Queries are built by chaining Python methods on the `QueryResult` object.
- Zero parsing cost
- Full IDE support
- Refactor-safe
- Debuggable
---
### 🔄 Runtime Evolution
Change structures, data, or even engine behavior **at runtime**.
No shutdowns.
No migrations.
No waiting.
---
### 🧱 Structure-Oriented Modeling
Define:
- node types
- relation types
- fields
- base types
- valid forms
Model your domain explicitly and safely.
---
### 🧬 Node Inheritance
Create base node types and extend them with shared properties and advanced relationships.
---
### ✨ Simple, Predictable Syntax
From defining structures to querying data, every step favors clarity and minimal syntax.
---
### 💾 Serializable
Persist the entire database into a single file.
---
## Installation
Install from **PyPI**:
```bash
pip install graphitedb
````
---
## Why Graphite?
Graphite was extracted from a **large production codebase** where Neo4j introduced more complexity than value.
Neo4j is a powerful tool — but in large projects, adding a separate graph database often increases:
* infrastructure complexity
* deployment cost
* maintenance burden
* cognitive load on developers
Graphite exists for cases where this cost is **not justified**.
It provides graph modeling **without adding another system to operate**.
---
## Example Usage
```python
import graphite
def example_complete_dsl_loading():
engine = graphite.engine()
complete_dsl = """
# Define node types
node Person
name: string
age: int
node User from Person
id: string
email: string
node Object
node Book from Object
title: string
n_pages: int
node Car from Object
model: string
year: int
# Define relation types
relation FRIEND both
Person - Person
since: date
relation OWNER reverse OWNED_BY
Person -> Object
since: date
purchased_at: date
relation AUTHOR reverse AUTHORED_BY
Person -> Book
year: int
# Create nodes
User, user_1, "Joe Doe", 32, "joe4030", "joe@email.com"
User, user_2, "Jane Smith", 28, "jane28", "jane@email.com"
User, user_3, "Bob Wilson", 45, "bob45", "bob@email.com"
User, user_4, "Alice Brown", 22, "alice22", "alice@email.com"
Book, book_1, "The Great Gatsby", 180
Book, book_2, "Python Programming", 450
Book, book_3, "Graph Databases", 320
Car, car_1, "Toyota Camry", 2020
Car, car_2, "Honda Civic", 2018
# Create relations
user_1 -[FRIEND, 2020-05-15]- user_2
user_1 -[FRIEND, 2019-08-22]- user_3
user_2 -[FRIEND, 2021-01-10]- user_4
user_1 -[OWNER, 2021-03-01, 2021-02-15]-> car_1
user_2 -[OWNER, 2019-06-20, 2019-05-10]-> book_1
user_3 -[OWNER, 2022-11-05, 2022-10-20]-> book_2
user_1 -[AUTHOR, 2020]-> book_3
user_2 -[AUTHOR, 2021]-> book_2
"""
engine.load_dsl(complete_dsl)
users = engine.query.User.get()
print([u["name"] for u in users])
return engine
```
More examples are available in `example.py` in the GitHub repository.