How I Built an Autonomous AI Agent That Earns USDC While I Sleep

Описание и применение

Автономный агент на Python, использующий локальный LLM Mistral‑7B‑Instruct, web3.py и протокол x402, который автоматически обрабатывает рыночные сигналы, принимает решения и получает USDC, работая без вмешательства человека на VPS за $5/мес.

Задача

Design an autonomous on‑chain agent that can perceive market signals, decide on actions, execute transactions, and receive USDC payments with minimal human oversight.

Результат

The agent runs continuously for 48 h without restart, maintains net profit ≥$0.005 per cycle, keeps decision‑to‑tx latency ≤2 s, and records every USDC receipt on‑chain for auditability.

Рабочий процесс

• 1. Signal Collector polls price feeds, sentiment APIs, and on‑chain events, normalising data into JSON.
• 2. Reasoning Module renders a short JSON‑only prompt, calls local Mistral‑7B‑Instruct via llama.cpp, and returns a structured decision.
• 3. Executor translates decision into a Base transaction (swap, liquidity provision, or API call), signs, estimates gas, and submits via web3.py.
• 4. Settlement & Accounting watches for incoming USDC, updates ledger, and optionally withdraws to external wallet.
• 5. Autonomous loop repeats every 5 s, with back‑pressure handling via asyncio.Queue and persistence of state in SQLite.

Доказательства

I selected Mistral‑7B‑Instruct quantised to 4‑bit using gguf and served locally with llama.cpp.

Источник

Decision‑to‑transaction latency should be low enough to capture short‑lived opportunities. ≤ 2 s from signal receipt to transaction submission.

Источник

The agent runs on a cheap virtual machine (≈ $5/month).

Источник