Build Your Own Gas Price Oracle with Python and Web3.py

Ever wanted to know the optimal gas price before submitting a transaction? Let'''s build a lightweight gas oracle that queries multiple sources and gives you the best estimate — all in pure Python.

Why Roll Your Own?

Centralized gas estimators can lag or go down. With your own oracle, you get:

• Redundancy across multiple data sources
• Custom logic (weighted averages, outlier removal)
• Zero API costs by using public RPC endpoints

The Code

import asyncio
from web3 import AsyncWeb3
from statistics import median

RPC_URLS = [
    "https://eth.llamarpc.com",
    "https://rpc.ankr.com/eth",
    "https://1rpc.io/eth",
]

async def fetch_gas_price(w3: AsyncWeb3) -> float:
    try:
        return float(await w3.eth.gas_price) / 1e9
    except Exception:
        return None

async def get_optimal_gas():
    prices = []
    for url in RPC_URLS:
        w3 = AsyncWeb3(AsyncWeb3.AsyncHTTPProvider(url))
        price = await fetch_gas_price(w3)
        if price:
            prices.append(price)

    if not prices:
        raise Exception("All RPCs failed")

    prices.sort()
    return round(median(prices), 2)

if __name__ == "__main__":
    gas = asyncio.run(get_optimal_gas())
    print(f"Recommended gas price: {gas} gwei")

Going Further

• Add support for EIP-1559 (maxFeePerGas / maxPriorityFeePerGas)
• Cache results to avoid rate-limiting
• Expose via a simple Flask/FastAPI endpoint
• Track historical gas trends for predictions

Open Source & Contributions

This pattern is part of a growing ecosystem of open-source Web3 tooling. Check out more projects and contribute at github.com/Byaigo — from smart contract utilities to DeFi dashboards, there'''s something for every builder.

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