Running Local LLM Agents on Mac Minis with Telegram Interface
A developer on r/LocalLLaMA detailed a system for running multiple local LLM agents on Mac Minis, controlled entirely through Telegram messages from a phone. The setup eliminates API costs and provides complete privacy while maintaining functionality similar to commercial services like Claude Code Channels.
Technical Setup
The core system uses:
- Local models through LMStudio: 35B models for everyday tasks, 235B models for heavier reasoning
- Claude Code running in tmux sessions on each Mac Mini
- Telegram bots that bridge user messages to the tmux sessions
- 80 lines of Python for the Telegram bot implementation (available on GitHub)
The workflow is straightforward: text a message to the Telegram bot, which types it into the tmux session, watches for output, and sends the response back.
Key Advantages
- Zero ongoing cost: Hardware is the only expense—no API keys, rate limits, or quota restrictions
- Complete privacy: Everything stays on the local area network (LAN)
- Model flexibility: Mix and match different models—one agent runs Gemini CLI, others use LMStudio pointed at Ollama models
- No vendor lock-in: LMStudio serves the Anthropic Messages API natively, so Claude Code connects to it as if talking to Anthropic's servers
Current Implementation
The developer runs 5 specialized agents, each with its own Telegram bot:
- Approval workflows with inline Telegram buttons (Approve/Reject/Tweak) for reviewing drafts from a phone
- Shared memory across agents via git synchronization
- Media generation (FLUX.1, Wan 2.2) dispatched to a GPU box
- Podcast pipeline with cloned voice TTS, triggered from a single Telegram message
Hardware Requirements
- 35B models: Run well on 64GB+ RAM Mac or 24GB GPU
- 235B models: Need 128-256GB RAM or multiple GPUs
- The developer recommends starting small and scaling as needed
The tmux bridge pattern is model-agnostic—it doesn't care what's running inside the session, allowing for easy swapping of underlying models. A full build guide for a single machine/agent is available, with multi-machine instructions coming soon.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Developer builds 6 iOS apps in 3 months using Claude Code, generates revenue
A developer used Claude Code to build and publish 6 iOS utility apps in 3 months, focusing on solving small real problems rather than perfection. The apps are now generating daily usage and revenue.
How One Developer Fixed 16 Architectural Weak Points in Their AI Agent System
A developer documented 16 architectural problems in their OpenClaw AI agent system and implemented specific fixes including explicit layer definitions, gateway authorization, and evidence-based execution.
Claude Sonnet 4.6 Grades Bug Reports from Four Qwen3.5 Local Models
A developer tested four Qwen3.5 variants by having them generate bug reports for an iOS game issue, then had Claude Sonnet 4.6 grade the reports. The models correctly identified a Swift bug where equipment border colors don't reset, but test code had compilation issues.
Claude Code Agents Negotiate API Contracts Without Orchestration Framework
Two Claude Code agents negotiated API contracts peer-to-peer using only two messaging tools and system prompts, agreeing on endpoint shapes, response formats, and CORS headers before writing code. The bridge implementation is ~190 lines of TypeScript with WebSocket broker and MCP channels.