by ma2za · Agent Tool · ★ 112
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Telegram LLM Bot Local-first Telegram AI bot for text, voice, tools, web search, and self-hosted storage. The default release runs through Ollama, keeps chat history in SQLite, transcribes voice locally with , archives voice audio to MinIO, and exposes readiness checks for the services that usually break during local or Docker deployment. What It Does Requirements Python 3.10 or newer Poetry Ollama Telegram bot token from BotFather Docker, if you want Compose-managed MinIO Pull the default model before starting the bot: Quickstart Install dependencies: Create starter config files: Set your Telegram token in : Start MinIO for voice audio archive: powershell docker compose
| Stars | 112 |
| Forks | 15 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.1131640600462/100 |
| Last Updated | 2026-07-01 |
| Created | 2023-08-07 |
| Platforms | browser, docker, python |
| Est. Tokens | ~15k |
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telegram-llm-bot is Local-first Telegram AI bot with Ollama tool calling, voice transcription, MinIO storage, SearchApi web search, and readiness checks. It is categorized as a Agent Tool with 112 GitHub stars.
telegram-llm-bot is primarily written in Python. It covers topics such as ai-bot, chatbot, docker-compose.
You can find installation instructions and usage details in the telegram-llm-bot GitHub repository at github.com/ma2za/telegram-llm-bot. The project has 112 stars and 15 forks, indicating an active community.
telegram-llm-bot is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to telegram-llm-bot on Agent Skills Hub include guaardvark, agentic-signal, captain-claw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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