thehardwarestore.ai

python-telegram-bot is a pure Python, asynchronous library that provides both low-level bindings to the Telegram Bot API and a high-level application framework for building Telegram bots. In production, it handles authentication, message sending/receiving, update processing, and bot lifecycle management. The library is used by developers to create bots that can respond to commands, messages, inline queries, callback queries, and other Telegram events, with support for payments, passports, polls, and more. The architecture is organized into three layers: the core `telegram` package containing data models for all Telegram API types (Chat, User, Message, etc.) with consistent serialization and equality semantics; the `telegram.ext` extension framework providing handlers (CommandHandler, MessageHandler, ConversationHandler), an Application class for orchestrating update processing, job queues, persistence, and rate limiting; and the `telegram.request` layer using httpx for async HTTP communication. The library uses async/await throughout and follows a handler-based event-driven pattern. Key entry points include `telegram.ext.Application` (built via `ApplicationBuilder`), `telegram.Bot` for direct API calls, and various handler classes. The primary data flow involves receiving updates from Telegram via polling or webhook, parsing them into `Update` objects, dispatching to registered handlers based on filters, and executing callback functions that can call Bot methods to respond. The library supports custom context types, persistent storage of bot/user/chat data, and scheduled jobs. External dependencies are minimal: `httpx` is the only required runtime dependency. Optional features require additional packages: `cryptography` for Telegram Passport, `APScheduler` for job queues, `cachetools` for callback data caching, `tornado` for webhook server, `aiolimiter` for rate limiting, and `pytz`/`zoneinfo` for timezone support. The library is extensively tested with both offline unit tests and online integration tests against the live Telegram API.

Freqtrade is a production-grade cryptocurrency trading bot written in Python. It provides a full-fledged platform for designing, backtesting, and executing automated trading strategies across major exchanges (Binance, Kraken, Bybit, OKX, etc.) via the CCXT library. The bot supports both spot and futures trading, dry-run simulation, and live deployment with docker and systemd integration. At its core, it exposes a plugin architecture for strategy definitions, pairlist filters, and risk protection modules, all configured through a unified YAML/JSON configuration system. It also includes an embedded REST API and WebSocket server for remote monitoring and control, with optional Telegram and Discord notification channels. Beyond live trading, freqtrade offers a comprehensive research subsystem: a deterministic backtesting engine with realistic fee/slippage modeling, hyperparameter optimization (hyperopt) using Optuna with over a dozen loss functions, and a machine learning module (FreqAI) that integrates sklearn, XGBoost, LightGBM, PyTorch, and reinforcement learning (SB3). The codebase includes tools for lookahead and recursive analysis to detect strategy biases, plus data download/convert/clean utilities supporting multiple storage formats (feather, parquet, JSON). Architecturally, the project follows a modular design with clear separation of concerns: exchange abstraction layer, strategy resolver, data provider, persistence (SQLAlchemy), and RPC multiplexing. It uses dependency injection through resolvers, a logging mixin for debounced messages, and a custom scheduler for periodic tasks. The test suite is extensive with over 800 test files covering all major components. While the codebase is large and some modules (e.g., exchange-specific subclasses) are tightly coupled to CCXT, many internal patterns such as the plugin system, resolver, and data handler interfaces are well-abstracted and could be extracted for reuse in other Python trading or data pipeline projects.

This repository is a comprehensive, production-oriented algorithmic trading framework implemented in TypeScript. It is organized as a monorepo with six interdependent packages: `trading-signals` (100+ technical indicator implementations with streaming `add`/`replace` semantics, each verified against external reference data like Tulip Indicators and Skender.Stock.Indicators), `trading-strategies` (strategy base classes, concrete strategies such as SMA crossover, scalp, trailing stop, and a multi-indicator confluence strategy, plus a backtesting engine and report generators), `exchange` (abstract broker interface with real implementations for Alpaca and Trading212, including mock broker for backtesting, candle batching, WebSocket streaming, and a CLI tool), `messaging` (a Telegram bot for managing accounts, strategies, watches, and reports via multi-step wizards), `candles` (labeled historical candle datasets for testing), and `trading-signals-docs` (a Next.js documentation site with interactive indicator demos and a visual backtester). The architecture emphasizes clean separation between signal generation, strategy logic, broker execution, and user interface. The indicator library is the core asset, providing a consistent contract (`IndicatorSeries`, `TechnicalIndicator`, `TrendIndicator`) with rich signal detection and state rollback. The strategy framework supports live trading and backtesting via the same advice-execution pipeline. The project is actively maintained with CI/CD, mutation testing, and a well-defined broker integration template. While the codebase shows high craftsmanship in its indicator implementations and test patterns, the large number of indicators and hand-derived test fixtures introduce maintenance overhead, and the tight coupling to Telegram limits platform portability.

The repository is a Kotlin library for the Telegram Bot API, providing a type-safe and easy-to-use wrapper for building Telegram bots. It features a dispatcher-based update handling system, custom Gson adapters for polymorphic types, and support for both polling and webhook approaches. The library is well-structured with clear separation of concerns, comprehensive test coverage, and modern Kotlin practices. The architecture includes network layer (Retrofit/OkHttp), data models (Kotlin data classes), dispatcher/handler system for update processing, and a DSL for declarative bot configuration.

Telebot is a comprehensive Go framework for building Telegram bots that provides a clean abstraction over the Telegram Bot API. It features a robust middleware system, flexible context handling for update processing, and innovative patterns like the Sendable interface that enables transparent file handling. The framework supports both long polling and webhook approaches for receiving updates, and includes comprehensive support for Telegram features including payments, inline queries, callbacks, and business accounts. The architecture emphasizes separation of concerns with distinct components for bot core functionality, message types, media handling, and update routing. Notable design patterns include the Context interface that encapsulates update data and action capabilities, the Sendable interface that allows types to define their own sending behavior, and a dual API pattern for options that combines convenience flags with comprehensive options structs.

This repository is a Java/Kotlin client library for the Telegram Bot API, providing type-safe wrappers for all bot API methods and models. The architecture is a classic library with a fluent builder pattern: users create request objects (e.g., `SendMessage`, `GetUpdates`), execute them via the `TelegramBot` class, and receive typed responses. The library handles both synchronous and asynchronous execution, supports long-polling and webhook modes, and includes Telegram Passport encryption/decryption utilities. The codebase is well-structured, with clear separation into model, request, response, and utility packages. It features custom Gson adapters for polymorphic API types (e.g., reaction types, chat boost sources) that demonstrate a clean discriminated union deserialization pattern. The library is actively maintained, tracking Telegram Bot API updates closely, and includes Kotlin extension functions for idiomatic usage. It is not a data research project; it is purely an integration library.

This repository implements a comprehensive Telegram bot for managing VPN subscriptions via Remnawave panel integration, featuring a web-based admin cabinet (cabinet) and public API (webapi). The system handles user subscriptions, payments (20+ gateways), referrals, promotional systems, contests, news, and support tickets. Architected with modular layers: Telegram bot handlers (aiogram), cabinet/web API routes (FastAPI), service-layer business logic, and SQLAlchemy ORM with Alembic migrations. Key entry points include main.py (application bootstrap), bot.py (Telegram setup), and unified_app.py (combined web server). Data flows involve Telegram interactions → handler processing → service execution → database CRUD operations, with webhook handlers for payment gateway callbacks and panel synchronization. External dependencies include Telegram Bot API, Remnawave API, PostgreSQL, Redis, SMTP, and numerous payment processors (Yookassa, CryptoBot, etc.). The codebase demonstrates strong engineering practices: clean separation of concerns, dependency injection, structured logging (structlog), and reusable patterns like event emitters, caching utilities, and retry queues. Payment processing abstraction shows particular skill with uniform CRUD interfaces across gateways. Active maintenance is evident through frequent releases, Dependabot integration, and responsive issue triage. While complexity arises from extensive feature set and payment integrations, the modular design contains coupling effectively. Technical drift is low due to current Python version (3.13), updated dependencies, and proactive schema migrations.

Searcharr is a Telegram bot application that interfaces with Sonarr and Radarr media management services. It allows users to authenticate, search for movies/TV shows, and add them to their media libraries through Telegram commands. The application features a modular architecture with separate layers for API communication, command handling, callback processing, and utilities. It includes robust authentication, conversation management for multi-step interactions, and internationalization support.

Telemirror is a Telegram message mirroring application that uses the client API (Telethon) to forward messages between channels, chats, and topics in real-time. It supports one-to-one, many-to-one, and many-to-many mappings with per-direction filter pipelines. The system handles new messages, edits, deletions, and albums, and can operate with either an in-memory cache (backed by an LRU dict) or a PostgreSQL database for persisting message ID mappings. It includes a plugin-based filter system for message transformation (URL removal, keyword replacement, forward formatting, content skipping) and patches Telethon's sending functions to support topic replies. The application is designed to run as a long-lived service with a health endpoint for PaaS deployment (Heroku, Docker). The codebase is well-structured with clean separation of concerns, async-first design, and modern Python features (dataclasses, protocols, enums, pattern matching).

This repository implements a curated directory of Chinese-language Telegram channels, groups, and bots. It combines a Python-based data pipeline (scraping, filtering, categorization, and README generation) with a modern Astro static site that provides search, category browsing, and PWA features. The system ingests links from multiple sources (existing README, tg-nav, GitHub Issues), applies strict filtering rules (language, harmful keywords, subscriber thresholds), and outputs both a human-readable README and a structured JSON consumed by the frontend. The architecture is a hybrid of data engineering and web application, with the data pipeline being the core value proposition. The frontend is well-crafted with SEO, analytics, offline support, and a clean UI. The codebase demonstrates solid engineering practices, including reusable filtering logic, component-based UI, and automated build steps.

This repository is the `pyTelegramBotAPI` Python library, providing both synchronous (`TeleBot`) and asynchronous (`AsyncTeleBot`) implementations for building Telegram bots. It acts as a wrapper around the Telegram Bot API, handling all message types, inline queries, payments, webhooks, polling, and more. The library's architecture is layered: a user-facing bot class that exposes method calls to a core API helper module (`apihelper.py` / `asyncio_helper.py`), which performs HTTP requests to Telegram servers. Bot logic is structured via message handlers (decorator-based), custom filters, middleware hooks, and a state machine with pluggable storage backends (memory, Redis, pickle). Key entry points include `TeleBot` and `AsyncTeleBot` classes, with webhook support via `SyncWebhookListener` and `AsyncWebhookListener` in the `ext` module. The library relies on `requests` (sync) and `aiohttp` (async) for HTTP, with optional dependencies `redis`, `Pillow`, `fastapi`, `uvicorn`, and `watchdog`. It is extensively tested and documented, with over 50 examples covering typical bot patterns. While the codebase is mature and well-maintained, it exhibits some technical drift: duplicated sync/async helper modules, deprecated API parameters maintained for backward compatibility, and incomplete localization files. Overall, it is a solid, production-ready library for Telegram bot development.

This repository is an Android application that bridges device events (SMS, MMS, missed calls, battery changes, app notifications) to a Telegram bot for monitoring and remote control. It uses the Telegram Bot API to forward events and long-polls for incoming commands to send SMS, USSD, list/read/delete SMS, fetch logcat, and more. The architecture is a monolith with multiple background services, broadcast receivers, and job scheduler jobs, all within a single Android app. It uses MMKV for key-value storage, OkHttp for HTTP, Gson for serialization, and libsodium for encryption. The codebase is well-structured with reusable utility classes (e.g., custom Mustache template engine, Telegram API wrapper, encryption helpers) and has unit tests for several components. It supports multi-language, QR code configuration import, and carbon-copy forwarding to external services via HAR format.

This repository is a curated collection of 280+ n8n workflow templates, organized into 18 categories covering email automation, AI agents, RAG pipelines, social media, DevOps, document processing, and more. It is not a software application itself but a library of importable JSON workflow definitions for the n8n automation platform. The templates demonstrate practical automation patterns such as webhook-triggered AI agents, scheduled data enrichment, human-in-the-loop approvals, and vector-store-based retrieval-augmented generation. While the collection is extensive and popular (25k+ stars), individual templates vary in quality, with many containing hardcoded placeholders, deprecated model references, and no automated testing. The repository serves as a valuable reference for n8n users seeking ready-made automation blueprints, but users must adapt credentials and verify compatibility with their n8n version.

ClawCloud-Run is a GitHub Actions-based automation script designed to automatically log into ClawCloudRun cloud service to maintain account activity. The system handles GitHub OAuth authentication, device verification, and two-factor authentication (via GitHub Mobile or Telegram-delivered TOTP codes). After login, it navigates to the console and apps pages to keep the session active, extracts the updated session cookie, and either updates it in GitHub Secrets or sends it via Telegram for manual update. The architecture consists of a workflow triggering a Python script that uses Playwright for browser automation, requests for API interactions, and pynacl for secret encryption. Key entry points are the GitHub Actions workflow (scheduled every 5 days) and the Python script's main execution loop.

FunPayVertex is a Telegram-controlled automation bot for the FunPay digital marketplace, written in Python. It scrapes FunPay's internal API to provide auto-delivery of digital goods, auto-reply to chat messages, auto-raise of listings, and inventory management (auto-restore/disable lots). The architecture is event-driven: the `FunPayAPI.Runner` polls FunPay's `/runner/` endpoint and emits typed events (new orders, messages, reviews) which are dispatched to registered handlers in `handlers.py` and through a plugin system. The Telegram bot (`tg_bot/`) serves as the primary user interface, offering menus for configuration, notifications, plugin management, and proxy settings. The codebase is modular with a separate `FunPayAPI` package that could be reused independently. It lacks tests and CI/CD, and relies on scraping HTML/CSS selectors that may break with site updates, contributing to a medium technical drift rating. The plugin system and localization framework are well-designed and potentially reusable.

This repository is a production-ready Telegram bot that manages a multi-protocol VPN/proxy server infrastructure deployed via Docker Compose. It supports WireGuard, Xray (VLESS), Shadowsocks, OpenConnect, NaiveProxy, Hysteria, MTProto, and others, plus AdGuard Home for DNS filtering and split tunneling via PAC files. The bot handles user subscriptions, configuration generation, service restarts, IP banning, and automated maintenance (cron, log analysis, backups). The architecture is monolithic: a single large PHP script (app/bot.php ~5000 lines) processes Telegram webhooks and executes commands on Docker containers via SSH. The codebase is functional and actively used (500+ stars) but suffers from lack of tests, tight coupling, hardcoded infrastructure assumptions, and security concerns (Docker socket exposure, SSH in every container). Reusable components are limited, with app/calc.php (CIDR calculation) being a standout isolated utility.