OpenLobster: Self-Hosted AI Agent in Go with 30MB RAM Footprint
OpenLobster is a self-hosted AI assistant built as a single Go binary that requires no Python environment, node_modules, or runtime management. The project was created to provide a personal AI agent that stays on local hardware and works with whatever model the user is running.
Technical Specifications
The agent uses 30MB RAM with all services loaded and has a 200ms cold start time. It runs on a Raspberry Pi without issues. The stack is built with Go + gqlgen for the backend and SolidJS + Vite for the frontend, licensed under GPL-3.0.
LLM Support
OpenLobster supports multiple LLM providers: OpenAI, Anthropic, Ollama, OpenRouter, Docker Model Runner, or anything with an OpenAI-compatible endpoint. Users pick one in Settings and can swap it out anytime.
Memory System
Memory is implemented as a proper graph database with two backends: Neo4j for full graph queries, or a local GML file backend for simpler setups that don't require running a database. The agent builds typed relationships as it learns, not just flat text dumps.
Multi-User Features
Multi-user functionality works properly with each person getting their own conversation history, memory, and tool permissions. Users can have different people on Telegram and Discord talking to the same agent without seeing each other's context.
Integration Capabilities
- MCP integration supports the full Streamable HTTP + OAuth 2.1 flow
- Per-user permission matrix per tool
- Marketplace for one-click integrations
- Core channels: Telegram, Discord, Slack, WhatsApp, SMS (not plugins)
Current Status
The project is still in beta with audio/multimodal features described as "rough around the edges." The local model support and low resource footprint are noted as solid features.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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