An LLM-powered agent that autonomously manages liquidity positions on Meteora DLMM for Solana.
Etemaro runs continuous screening and management cycles, deploying capital into high-quality Meteora DLMM pools and closing positions based on live PnL, yield, and range data — all driven by an LLM reasoning over real on-chain state instead of following a fixed rule set.
- LLM-driven ReAct loop — The agent inspects live pool and position data, reasons about risk and yield, then calls tools to deploy, manage, or close positions.
- HiveMind collective learning — Agents share lessons and performance events across a fleet, so every instance benefits from what others have learned.
- Dry-run safe simulation — Test strategies against real on-chain data without spending gas; mock positions are tracked locally.
- Multi-surface interface — CLI for one-shot commands, a Telegram bot for remote control, and a cross-platform desktop app.
- Strategy library + signal adaptation — Preset LP strategies with configurable bin distribution; signal weights evolve based on closed-position performance.
git clone https://github.com/romankurnovskii/etemaro
cd etemaro
npm install
npm run setup
npm run devOption A: PM2 (production process manager)
npm run build
npm run pm2:start # Start daemon under PM2 with auto-restart
npm run pm2:logs # Tail live logsOption B: Docker
# Development (hot reload, mounts source)
docker compose -f docker-compose.dev.yml up --build
# Production (on remote server, .env already present)
docker compose -f docker-compose.prod.yml up -d --build --force-recreate --remove-orphans