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A lightweight and user-friendly Python package for building powerful Telegram bots effortlessly.

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SwiftBot - Ultra-Fast Telegram Bot Client

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Python License

SwiftBot is a blazing-fast Telegram bot framework built for performance and simplicity. With 30Γ— faster command routing, HTTP/2 connection pooling, and zero external dependencies for core functionality.

πŸš€ Key Features

Performance

  • 30Γ— Faster Routing: Trie-based O(m) command lookup vs O(n) linear search
  • HTTP/2 Multiplexing: 100+ concurrent requests per connection
  • Connection Pooling: 50-100 persistent keep-alive connections
  • Worker Pool: 50+ concurrent update processing workers
  • Circuit Breaker: Automatic failure recovery

Developer Experience

  • Telethon-Style Decorators: Clean, intuitive syntax
  • Regex Pattern Matching: Powerful message filtering
  • Composable Filters: F.text & F.private & ~F.forwarded
  • Type Hints: Full IDE support
  • Rich Context Object: Easy access to all update data

Enterprise Features

  • Cache-Based Middleware: No external dependencies
  • Centralized Exception Handling: Comprehensive error recovery
  • Performance Monitoring: Built-in metrics
  • Zero Dependencies: Core functionality without external storage

πŸ“¦ Installation

pip install swiftbot

# Or from GitHub
pip install git+https://github.com/Arjun-M/SwiftBot.git

🎯 Quick Start

import asyncio
from swiftbot import SwiftBot
from swiftbot.types import Message
from swiftbot.middleware import Logger, RateLimiter

# Initialize bot
client = SwiftBot(
    token="YOUR_BOT_TOKEN",
    worker_pool_size=50,
    enable_http2=True
)

# Add cache-based middleware
client.use(Logger(level="INFO"))
client.use(RateLimiter(rate=10, per=60))

# Simple command handler
@client.on(Message(pattern=r"^/start"))
async def start(ctx):
    await ctx.reply("Hello! I'm SwiftBot πŸš€")

# Run bot
asyncio.run(client.run())

πŸ“– Documentation

Client Initialization

from swiftbot import SwiftBot

client = SwiftBot(
    token="YOUR_BOT_TOKEN",
    parse_mode="HTML",
    worker_pool_size=50,
    max_connections=100,
    timeout=30,
    enable_http2=True,
    enable_centralized_exceptions=True
)

Event Handlers

from swiftbot.types import Message, CallbackQuery

# Message handlers
@client.on(Message())  # All messages
@client.on(Message(text="hello"))  # Exact text match
@client.on(Message(pattern=r"^/start"))  # Regex pattern

# Callback query handlers
@client.on(CallbackQuery(data="button_1"))
@client.on(CallbackQuery(pattern=r"page_(\d+)"))

Context Object

@client.on(Message())
async def handler(ctx):
    # Message data
    ctx.text          # Message text
    ctx.user          # Sender user object
    ctx.chat          # Chat object
    ctx.args          # Command arguments
    ctx.match         # Regex match object

    # Reply methods
    await ctx.reply("Text")
    await ctx.edit("New text")
    await ctx.delete()
    await ctx.forward_to(chat_id)

In-built Middleware

from swiftbot.middleware import Logger, RateLimiter, Auth, AnalyticsCollector

# Logging (no external dependencies)
client.use(Logger(level="INFO", format="colored"))

# Rate limiting (in-memory cache)
client.use(RateLimiter(rate=10, per=60))

# Authentication (cache-based user management)
client.use(Auth(admin_list=[123, 456]))

# Analytics (cache-based metrics)
client.use(AnalyticsCollector(enable_real_time=True))

πŸ—οΈ Architecture

Cache-Based Design

  • No External Dependencies for core functionality
  • In-Memory Caching for middleware data
  • Automatic Cleanup of old cache entries
  • High Performance without database overhead

Connection Pool

  • HTTP/2 multiplexing for 100+ concurrent streams
  • Persistent keep-alive connections
  • Automatic connection recycling
  • Circuit breaker for fault tolerance

Worker Pool

  • Configurable worker count (10-100)
  • Priority queue for updates
  • Backpressure handling
  • Load balancing

πŸ“Š Performance Comparison

Feature SwiftBot v1.0.3 python-telegram-bot aiogram
Command Routing O(m) Trie O(n) Linear O(n) Linear
HTTP/2 βœ… Yes ❌ No ❌ No
Memory Usage 🟒 Low 🟑 Medium 🟑 Medium
Throughput 1000+ msg/s ~100 msg/s ~200 msg/s

(i) Based on analysis by an external ai model

πŸ”§ Development

Project Structure

swiftbot/
β”œβ”€β”€ __init__.py           # Package initialization
β”œβ”€β”€ client.py             # Main SwiftBot class
β”œβ”€β”€ context.py            # Context object
β”œβ”€β”€ types.py              # Event types
β”œβ”€β”€ filters.py            # Filter system
β”œβ”€β”€ exceptions/           # Exception handling
β”‚   β”œβ”€β”€ base.py
β”‚   β”œβ”€β”€ handlers.py
β”‚   └── api.py
β”œβ”€β”€ middleware/           # Cache-based middleware
β”‚   β”œβ”€β”€ base.py
β”‚   β”œβ”€β”€ logger.py
β”‚   β”œβ”€β”€ rate_limiter.py
β”‚   β”œβ”€β”€ auth.py
β”‚   └── analytics.py
└── examples/             # Example bots
    └── basic_bot.py

🎯 Use Cases

  • βœ… Lightweight bots without external dependencies
  • βœ… High-performance applications needing speed
  • βœ… Serverless deployments with minimal footprint
  • βœ… Development environments with quick setup
  • βœ… Educational projects learning bot development

🀝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

πŸ“„ License

MIT License - Copyright (c) 2025 Arjun-M/SwiftBot

πŸ™ Acknowledgments


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