DkBotzDB Is The Most Powerful And Developer-Friendly Database Solution Powered By DKBotz And DkBotzPro, Offering Fast, Reliable, Scalable, And Secure Data Management. Designed For Effortless Integration And Built With Modern API Standards, DkBotzDB Ensures Smooth Performance Across Applications, Backed By The Trust And Excellence of dkbotzpro.in.
Welcome to DKBOTZDB! 🚀
DKBOTZDB Is The Most Powerful And Developer-Friendly Database Solution Powered By DKBOTZ And DKBOTZPro, Offering Fast, Reliable, Scalable, And Secure Data Management. Designed For Effortless Integration And Built With Modern API Standards, dkbotzdb Ensures Smooth Performance Across Applications, Backed By The Trust And Excellence of dkbotzpro.in.
After years of struggling with complex database setups, poor documentation, and unreliable services, we created dkbotzdb to solve real developer problems:
We were fed up with databases that produce promises but deliver headaches. There were too many occasions where we were struggling against:
So we decided to build something better. dkbotzdb is our answer to these problems.
When we say fast, we mean fast. This is not seconds, we are taking about milliseconds. As such, we have optimized the query path, and built intelligent caching mechanisms to guarantee it will still be responsive under heavy load.
Do you recall the last time you started using a new service and it just worked out-of-the-box? That's what we're aiming for with dkbotzdb; start coding with just a few lines of code. No configuration files or multi-step environment setup.
We understand that your data matters. Therefore, we designed multiple layers of redundancy, automatic failover processes and backup processes to make sure that your data is safe and applications are online.
Sometimes you just need CRUD operations. Other times you want complex aggregations, regex matching and range filters. dkbotzdb makes everything easy to query. dkbotzdb has a powerful query system, but it is also easy enough for a beginner to figure out.
All new features written for dkbotzdb go through one simple filter: "Will this make a developer's life easier?" If no, the feature doesn't get shipped. That's why you will see colored console logging, intuitive method names, and detailed error messages throughout the entire library.
You can install dkbotzdb using pip:
pip install dkbotzdb
You can also install DKBOTZDB by cloning this repository:
# Clone the repository
git clone https://github.com/DKBOTZPROJECT/DKBOTZDB.git
cd DKBOTZDB
# Install dependencies
pip install requests colorlog
# Or install from requirements.txt
pip install -r requirements.txt
from dkbotzdb import DKBOTZDB
# Method 1: Bracket notation (recommended)
db = DKBOTZDB()['YOUR_TOKEN']['your_collection']
# Method 2: Attribute access
db = DKBOTZDB().YOUR_TOKEN.your_collection
# Method 3: Traditional initialization
db = DKBOTZDB(token='YOUR_TOKEN')
db.collection = 'your_collection'
# Insert your first document
result = db.insert_one({"name": "John", "age": 30, "city": "New York"})
print("Document inserted:", result)
# Find the document
user = db.find_one({"name": "John"})
print("Found user:", user)
# All these work the same way
from dkbotzdb import DKBOTZDB, DkBotzDB, DKBOTZdb
db1 = DKBOTZDB()['token']['collection']
db2 = DkBotzDB()['token']['collection']
db3 = DKBOTZdb()['token']['collection']
from dkbotzdb import DKBOTZDB
# Method 1: Bracket notation
db = DKBOTZDB()['YOUR_TOKEN']['users']
# Method 2: Attribute access
db = DKBOTZDB().YOUR_TOKEN.users
# Method 3: Mixed approach
db = DKBOTZDB()['YOUR_TOKEN'].users
db = DKBOTZDB().YOUR_TOKEN['users']
# Method 4: Traditional way
db = DKBOTZDB(token='YOUR_TOKEN')
db.collection = 'users'
Insert a single document into the collection.
# Basic insertion
user_data = {
"name": "Alice Johnson",
"email": "alice@example.com",
"age": 28,
"department": "Engineering",
"skills": ["Python", "JavaScript", "React"],
"salary": 75000,
"joined_date": "2023-01-15"
}
result = db.insert_one(user_data)
print("Inserted:", result)
# Using alias
result = db.insertOne(user_data)
Insert multiple documents at once.
users_data = [
{"name": "Bob Smith", "age": 32, "department": "Marketing", "salary": 65000},
{"name": "Carol White", "age": 29, "department": "Sales", "salary": 60000},
{"name": "David Brown", "age": 35, "department": "Engineering", "salary": 80000},
{"name": "Eve Davis", "age": 26, "department": "HR", "salary": 55000}
]
result = db.insert_many(users_data)
print("Inserted multiple:", result)
# Using alias
result = db.insertMany(users_data)
Find a single document matching the query.
# Find by name
user = db.find_one({"name": "Alice Johnson"})
print("Found user:", user)
# Find by multiple criteria
engineer = db.find_one({"department": "Engineering", "age": {"$gte": 30}})
print("Found engineer:", engineer)
# Find without query (returns first document)
first_user = db.find_one()
print("First user:", first_user)
# Using alias
user = db.findOne({"name": "Bob Smith"})
Find multiple documents matching the query.
# Find all engineers
engineers = db.find({"department": "Engineering"})
print("All engineers:", engineers)
# Find with age range
young_employees = db.find({"age": {"$lt": 30}})
print("Young employees:", young_employees)
# Find with multiple conditions
high_earners = db.find({
"salary": {"$gte": 70000},
"department": {"$in": ["Engineering", "Marketing"]}
})
print("High earners:", high_earners)
# Find with limit and skip
limited_results = db.find(
query={"department": "Engineering"},
limit=5,
skip=2,
sort={"salary": -1} # Sort by salary descending
)
print("Limited results:", limited_results)
# Find all documents
all_users = db.find()
print("All users:", all_users)
Update a single document.
# Update specific field
result = db.update_one(
{"name": "Alice Johnson"},
{"$set": {"age": 29, "salary": 78000}}
)
print("Update result:", result)
# Add new field
result = db.update_one(
{"name": "Bob Smith"},
{"$set": {"remote_work": True, "last_updated": "2024-01-15"}}
)
# Increment a value
result = db.update_one(
{"name": "Carol White"},
{"$inc": {"salary": 5000}}
)
# Using alias
result = db.updateOne({"name": "David Brown"}, {"$set": {"department": "DevOps"}})
Update multiple documents.
# Update all engineers
result = db.update_many(
{"department": "Engineering"},
{"$set": {"remote_work": True, "updated": "2024-01-15"}}
)
print("Updated engineers:", result)
# Give raise to all employees with salary < 70000
result = db.update_many(
{"salary": {"$lt": 70000}},
{"$inc": {"salary": 5000}}
)
print("Salary updates:", result)
# Using alias
result = db.updateMany(
{"age": {"$gte": 30}},
{"$set": {"senior_employee": True}}
)
Replace an entire document.
# Replace entire document
new_user_data = {
"name": "Alice Johnson",
"email": "alice.johnson@newcompany.com",
"age": 29,
"department": "Data Science",
"skills": ["Python", "Machine Learning", "SQL"],
"salary": 85000,
"joined_date": "2024-01-01"
}
result = db.replace_one(
{"name": "Alice Johnson"},
new_user_data
)
print("Replace result:", result)
# Using alias
result = db.replaceOne({"name": "Bob Smith"}, new_user_data)
Delete a single document.
# Delete by name
result = db.delete_one({"name": "Eve Davis"})
print("Delete result:", result)
# Delete by multiple criteria
result = db.delete_one({
"department": "HR",
"age": {"$lt": 25}
})
# Using alias
result = db.deleteOne({"name": "Carol White"})
Delete multiple documents.
# Delete all interns
result = db.delete_many({"department": "Intern"})
print("Deleted interns:", result)
# Delete employees with low salary
result = db.delete_many({"salary": {"$lt": 50000}})
print("Deleted low salary employees:", result)
# Delete by age range
result = db.delete_many({"age": {"$gte": 65}})
# Using alias
result = db.deleteMany({"active": False})
Count documents matching a query.
# Count all documents
total_count = db.count_documents()
print("Total employees:", total_count)
# Count engineers
engineer_count = db.count_documents({"department": "Engineering"})
print("Total engineers:", engineer_count)
# Count with complex query
high_earner_count = db.count_documents({
"salary": {"$gte": 75000},
"age": {"$lt": 40}
})
print("Young high earners:", high_earner_count)
# Using aliases
count1 = db.countDocuments({"department": "Sales"})
count2 = db.count({"remote_work": True})
Advanced querying with multiple filter options.
# Basic aggregate query
results = db.aggregate(
query={"department": "Engineering"},
sort={"salary": -1},
limit=10
)
print("Top 10 engineers by salary:", results)
# Aggregate with regex search
results = db.aggregate(
regex={"name": "^A.*"}, # Names starting with 'A'
sort={"age": 1}
)
print("Names starting with A:", results)
# Aggregate with range filter
results = db.aggregate(
range_filter={"salary": {"min": 60000, "max": 80000}},
sort={"joined_date": -1}
)
print("Mid-range salaries:", results)
# Complex aggregate query
results = db.aggregate(
query={"department": {"$in": ["Engineering", "Data Science"]}},
regex={"skills": "Python"},
range_filter={"age": {"min": 25, "max": 35}},
sort={"salary": -1},
limit=5,
skip=2
)
print("Complex query results:", results)
# Count only mode
count = db.aggregate(
query={"department": "Engineering"},
count_only=True
)
print("Engineer count:", count)
# Using smart_find alias
results = db.smart_find(
query={"remote_work": True},
sort={"salary": -1},
limit=20
)
Get distinct values for a field.
# Get all unique departments
departments = db.distinct("department")
print("All departments:", departments)
# Get unique skills with filter
skills = db.distinct("skills", {"department": "Engineering"})
print("Engineering skills:", skills)
# Get unique ages
ages = db.distinct("age")
print("All ages:", ages)
# Get unique cities where employees live
cities = db.distinct("city", {"salary": {"$gte": 70000}})
print("Cities of high earners:", cities)
List all collections in your database.
# Get all collection names
all_collections = db.collections()
print("All collections:", all_collections)
# Example output: ['users', 'products', 'orders', 'logs']
Drop (delete) the current collection.
# Drop current collection
result = db.drop()
print("Collection dropped:", result)
# Note: This will permanently delete all data in the collection
Drop all collections in the database.
# Drop all collections (use with extreme caution!)
result = db.drop_all()
print("All collections dropped:", result)
# Using alias
result = db.dropall()
# Warning: This will delete ALL your data permanently!
Get usage statistics for your database.
# Get usage information
usage = db.usage_info()
print("Usage stats:", usage)
# Example output might include:
# {
# "total_collections": 5,
# "total_documents": 1500,
# "storage_used": "2.5MB",
# "api_calls_today": 250,
# "plan_limits": {...}
# }
Get information about a token.
# Get current token info
token_info = db.get_token_info()
print("Token info:", token_info)
# Check another token
other_token_info = db.get_token_info("other_token_here")
print("Other token info:", other_token_info)
# Using aliases
info1 = db.token_info()
info2 = db.token_details("some_token")
# Example output:
# {
# "token_id": "abc123",
# "name": "My App Token",
# "plan": "Pro",
# "expires": "2024-12-31",
# "created": "2024-01-01",
# "status": "active"
# }
Check plan status for a token.
# Check current token's plan
plan = db.plan_status()
print("Plan status:", plan)
# Check specific token's plan
plan = db.plan_status("user_token_here")
print("User plan:", plan)
# Using aliases
plan1 = db.check_plan()
plan2 = db.user_plan("another_token")
# Example output:
# {
# "plan_name": "Pro",
# "expires": "2024-12-31",
# "api_calls_remaining": 9750,
# "storage_limit": "100GB",
# "collections_limit": 50
# }
List all available plans.
# Get all available plans
available_plans = db.list_plans()
print("Available plans:", available_plans)
# Using aliases
plans1 = db.plans()
plans2 = db.get_plans()
# Example output:
# [
# {
# "plan_id": "free",
# "name": "Free",
# "price": 0,
# "api_calls": 1000,
# "storage": "10MB"
# },
# {
# "plan_id": "pro",
# "name": "Pro",
# "price": 29,
# "api_calls": 100000,
# "storage": "100GB"
# }
# ]
Note: These operations require admin privileges
Generate a new token (admin only).
# Generate new token
new_token = db.generate_token(
name="New User Token",
plan_id="pro",
duration_days=365
)
print("New token created:", new_token)
# Using aliases
token1 = db.create_token("API Token", "free", 30)
token2 = db.admin_token("Admin Token", "enterprise", 730)
# Example output:
# {
# "token": "new_generated_token_here",
# "name": "New User Token",
# "plan": "pro",
# "expires": "2025-01-15"
# }
Activate a plan for a user (admin only).
# Activate plan for user
result = db.activate_plan(
user_token="user_token_here",
plan_id="pro",
duration_days=365
)
print("Plan activated:", result)
# Activate different plan
result = db.activate_plan(
user_token="another_user_token",
plan_id="enterprise",
duration_days=730
)
dkbotzdb provides multiple aliases for the same operations to match different coding styles:
| Primary Method | Aliases |
|---|---|
insert_one() | insertOne() |
insert_many() | insertMany() |
find_one() | findOne() |
update_one() | updateOne() |
update_many() | updateMany() |
delete_one() | deleteOne() |
delete_many() | deleteMany() |
replace_one() | replaceOne() |
count_documents() | countDocuments(), count() |
smart_find() | aggregate() |
drop_all() | dropall() |
get_token_info() | token_info(), token_details() |
plan_status() | check_plan(), user_plan() |
list_plans() | plans(), get_plans() |
generate_token() | create_token(), admin_token() |
dkbotzdb uses beautiful colored logging for better debugging experience:
import logging
from dkbotzdb import logger
# Set different log levels
logger.setLevel(logging.DEBUG) # Show all messages
logger.setLevel(logging.WARNING) # Show only warnings and errors
logger.setLevel(logging.ERROR) # Show only errors
dkbotzdb includes robust error handling with automatic retry logic:
from dkbotzdb import DKBOTZDB
db = DKBOTZDB()['YOUR_TOKEN']['test_collection']
# The library automatically handles:
# - Network timeouts (retries up to 3 times)
# - Connection errors (with exponential backoff)
# - Invalid JSON responses
# - API rate limits
# - Server errors
# Always check results
result = db.insert_one({"test": "data"})
if result:
print("Success:", result)
else:
print("Operation failed - check logs for details")
# For critical operations, you might want additional checks
user = db.find_one({"id": "important_user"})
if user is None:
print("User not found or error occurred")
# Handle the error case
else:
print("User found:", user)
Here's a comprehensive example showing various operations:
from dkbotzdb import DKBOTZDB
import json
# Initialize database
db = DKBOTZDB()['YOUR_TOKEN']['employees']
# Sample data
employees = [
{
"id": 1,
"name": "John Doe",
"email": "john@company.com",
"department": "Engineering",
"position": "Senior Developer",
"salary": 85000,
"skills": ["Python", "JavaScript", "Docker"],
"remote": True,
"joined": "2022-03-15"
},
{
"id": 2,
"name": "Jane Smith",
"email": "jane@company.com",
"department": "Marketing",
"position": "Marketing Manager",
"salary": 72000,
"skills": ["SEO", "Content Marketing", "Analytics"],
"remote": False,
"joined": "2021-08-22"
},
{
"id": 3,
"name": "Mike Johnson",
"email": "mike@company.com",
"department": "Engineering",
"position": "DevOps Engineer",
"salary": 90000,
"skills": ["AWS", "Kubernetes", "Python"],
"remote": True,
"joined": "2023-01-10"
}
]
def main():
# Insert employees
print("=== Inserting Employees ===")
result = db.insert_many(employees)
print(f"Inserted: {result}")
# Count total employees
print("\n=== Counting Employees ===")
total = db.count_documents()
print(f"Total employees: {total}")
# Find all engineers
print("\n=== Finding Engineers ===")
engineers = db.find({"department": "Engineering"})
print(f"Engineers: {json.dumps(engineers, indent=2)}")
# Find remote workers
print("\n=== Finding Remote Workers ===")
remote_workers = db.smart_find(
query={"remote": True},
sort={"salary": -1}
)
print(f"Remote workers: {json.dumps(remote_workers, indent=2)}")
# Update salary
print("\n=== Updating Salary ===")
update_result = db.update_one(
{"name": "John Doe"},
{"$inc": {"salary": 5000}}
)
print(f"Salary update: {update_result}")
# Get distinct departments
print("\n=== Distinct Departments ===")
departments = db.distinct("department")
print(f"Departments: {departments}")
# Advanced search
print("\n=== Advanced Search ===")
high_earners = db.aggregate(
query={"salary": {"$gte": 80000}},
regex={"skills": "Python"},
sort={"salary": -1}
)
print(f"High-earning Python developers: {json.dumps(high_earners, indent=2)}")
# Get usage info
print("\n=== Usage Info ===")
usage = db.usage_info()
print(f"Usage: {json.dumps(usage, indent=2)}")
if __name__ == "__main__":
main()
Let me show you how this all comes together in a real application:
from dkbotzdb import DKBOTZDB
import json
from datetime import datetime
# Initialize our database
db = DKBOTZDB()['YOUR_TOKEN']['employee_management']
class EmployeeManager:
def __init__(self):
self.db = db
def add_employee(self, employee_data):
"""Add a new employee to the system"""
# Add timestamp
employee_data['created_at'] = datetime.now().isoformat()
employee_data['status'] = 'active'
result = self.db.insert_one(employee_data)
if result:
print(f"✅ Successfully added {employee_data['name']}")
return result
else:
print("❌ Failed to add employee")
return None
def find_employees_by_department(self, department):
"""Get all employees in a specific department"""
employees = self.db.find({"department": department, "status": "active"})
return employees or []
def promote_employee(self, employee_id, new_position, new_salary):
"""Promote an employee"""
update_data = {
"$set": {
"position": new_position,
"salary": new_salary,
"last_promotion": datetime.now().isoformat()
}
}
result = self.db.update_one({"employee_id": employee_id}, update_data)
if result:
print(f"🎉 Employee {employee_id} promoted to {new_position}")
return result
def get_department_stats(self):
"""Get statistics about each department"""
departments = self.db.distinct("department")
stats = {}
for dept in departments:
count = self.db.count_documents({"department": dept, "status": "active"})
avg_salary = self.db.aggregate(
query={"department": dept, "status": "active"},
count_only=False
)
stats[dept] = {
"employee_count": count,
"employees": avg_salary
}
return stats
def search_employees(self, search_term):
"""Smart search across multiple fields"""
return self.db.smart_find(
regex={
"$or": [
{"name": f".*{search_term}.*"},
{"email": f".*{search_term}.*"},
{"skills": f".*{search_term}.*"}
]
},
sort={"name": 1}
)
# Example usage
if __name__ == "__main__":
manager = EmployeeManager()
# Add a new employee
new_employee = {
"employee_id": "EMP001",
"name": "Jennifer Walsh",
"email": "jennifer@company.com",
"department": "Engineering",
"position": "Software Developer",
"salary": 75000,
"skills": ["Python", "React", "PostgreSQL"],
"remote_work": True
}
manager.add_employee(new_employee)
# Find all engineers
engineers = manager.find_employees_by_department("Engineering")
print(f"Engineers on the team: {len(engineers)}")
# Promote someone
manager.promote_employee("EMP001", "Senior Software Developer", 85000)
# Get department statistics
stats = manager.get_department_stats()
print("Department statistics:", json.dumps(stats, indent=2))
# Search for someone
results = manager.search_employees("Jennifer")
print("Search results:", results)
| Feature | dkbotzdb | MongoDB | Firebase | Supabase |
|---|---|---|---|---|
| Setup Time | 30 seconds | 30 minutes | 5 minutes | 10 minutes |
| Learning Curve | Minimal | Steep | Moderate | Moderate |
| Pricing | Pay-as-you-go | Complex tiers | Limited free | Limited free |
| Real-time | ✅ (Coming Q2) | ✅ | ✅ | ✅ |
| ACID Transactions | ✅ (Coming Q3) | ✅ | Limited | ✅ |
| GraphQL | ✅ (Coming Q2) | Manual setup | ❌ | ✅ |
| Multi-language | ✅ (Coming Q1 '25) | ✅ | ✅ | ✅ |
| Serverless | ✅ (Coming Q1 '25) | ❌ | ✅ | ✅ |
| Developer Experience | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
from flask import Flask, request, jsonify
from dkbotzdb import DKBOTZDB
app = Flask(__name__)
db = DKBOTZDB()['YOUR_TOKEN']['users']
@app.route('/users', methods=['GET'])
def get_users():
users = db.find()
return jsonify(users or [])
@app.route('/users', methods=['POST'])
def create_user():
user_data = request.json
result = db.insert_one(user_data)
if result:
return jsonify({"success": True, "data": result}), 201
return jsonify({"error": "Failed to create user"}), 400
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from dkbotzdb import DKBOTZDB
app = FastAPI()
db = DKBOTZDB()['YOUR_TOKEN']['products']
class Product(BaseModel):
name: str
price: float
category: str
@app.get("/products")
async def get_products():
products = db.find()
return products or []
@app.post("/products")
async def create_product(product: Product):
result = db.insert_one(product.dict())
if result:
return {"success": True, "data": result}
raise HTTPException(status_code=400, detail="Failed to create product")
import discord
from discord.ext import commands
from dkbotzdb import DKBOTZDB
bot = commands.Bot(command_prefix='!')
db = DKBOTZDB()['YOUR_TOKEN']['discord_users']
@bot.event
async def on_ready():
print(f'{bot.user} has connected to Discord!')
@bot.command(name='register')
async def register_user(ctx, *, name):
user_data = {
"discord_id": str(ctx.author.id),
"username": str(ctx.author),
"display_name": name,
"server_id": str(ctx.guild.id)
}
result = db.insert_one(user_data)
if result:
await ctx.send(f"✅ {name} registered successfully!")
else:
await ctx.send("❌ Registration failed!")
bot.run('YOUR_BOT_TOKEN')
import os
from dotenv import load_dotenv
# Use environment variables
load_dotenv()
DB_TOKEN = os.getenv('DKBOTZDB_TOKEN')
# Never hardcode tokens
# ❌ Bad
db = DKBOTZDB()['hardcoded_token_123']['collection']
# ✅ Good
db = DKBOTZDB()[DB_TOKEN]['collection']
import re
import html
def sanitize_input(data):
"""Sanitize user input"""
if isinstance(data, str):
# Remove HTML tags
data = html.escape(data)
# Remove potential injection patterns
data = re.sub(r'[<>"\';]', '', data)
elif isinstance(data, dict):
return {k: sanitize_input(v) for k, v in data.items()}
elif isinstance(data, list):
return [sanitize_input(item) for item in data]
return data
# Usage
safe_data = sanitize_input(user_input)
db.insert_one(safe_data)
After having used dkbotzdb in production on various projects, here's what we can share that will save you time:
Always Check Return Values: Network issues are commonplace. Always check that it worked before moving on.
Use Batch Operations: Rather than repeated calls to insert_one() fifty times, use insert_many() and pass it in fifty. It will be incredibly faster.
Consider Structure of Your Queries: Well structured queries will run faster and can often result in less api calls.
Monitor Your Usage: usage_info() is helpful to avoid surprises and monitor your api usage.
Build Error Handling & Retries: Your most critical operations should have retry logic.
Keep your tokens secure: Don't stick tokens in version control, use environment variables.
Use Descriptive Collection Names: user_profiles is better than users is better than data.
insert_many() over multiple insert_one() callsusage_info()We welcome contributions to improve DKBOTZDB. To contribute:
git checkout -b feature-name)git commit -am 'Add new feature')git push origin feature-name)pip install DKBOTZdbfrom dkbotzdb import DKBOTZDB
# Your journey starts here
db = DKBOTZDB()['YOUR_TOKEN']['my_first_collection']
# Welcome to the future of database management
welcome = {
"message": "Welcome to DKBOTZDB!",
"status": "Ready to build amazing things",
"excited": True
}
result = db.insert_one(welcome)
print("🎉 You're all set! Let's build something incredible together!")
This project is licensed under the GNU Affero General Public License v3.0.
What this means:
See the LICENSE file for complete details.
Made By DKBOTZ Team ❤️
FAQs
DkBotzDB Is The Most Powerful And Developer-Friendly Database Solution Powered By DKBotz And DkBotzPro, Offering Fast, Reliable, Scalable, And Secure Data Management. Designed For Effortless Integration And Built With Modern API Standards, DkBotzDB Ensures Smooth Performance Across Applications, Backed By The Trust And Excellence of dkbotzpro.in.
We found that dkbotzdb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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