Maximal Extractable Value (MEV) bots continue to be a focal point for crypto developers building on Ethereum and other EVM-compatible chains, plus Solana. Whether your goal is to capture arbitrage, liquidation opportunities, or front-run profitable trades, understanding the plumbing of MEV bot development is essential.
In my experience, the biggest hurdle isn't conceptual: MEV as a concept is well documented. Instead, it's about wiring up reliable bundles, interacting securely with mempools, and managing execution latency. This tutorial aims to provide practical stepping stones for developers looking to ramp up quickly.
We'll focus on tooling like Flashbots for Ethereum and Jito for Solana, plus Artemis as an emerging Rust MEV framework. Throughout, expect runnable examples, step-by-step setup, security caveats, and architecture notes.
Flashbots has become the de facto standard for Ethereum MEV searchers. It bypasses public mempool race conditions using private bundles submitted directly to miners. Here’s how to get started with a basic Flashbots searcher:
Install the Flashbots ethers provider packages:
npm install @flashbots/ethers-provider-bundle ethers
Sample searcher skeleton:
import { FlashbotsBundleProvider } from '@flashbots/ethers-provider-bundle';
import { ethers } from 'ethers';
async function main() {
const provider = new ethers.providers.JsonRpcProvider('https://goerli.infura.io/v3/YOUR_INFURA_KEY');
const wallet = new ethers.Wallet('YOUR_PRIVATE_KEY', provider);
const flashbotsProvider = await FlashbotsBundleProvider.create(provider, wallet);
const blockNumber = await provider.getBlockNumber();
const tx = {
to: '0xReceiverAddress',
value: ethers.utils.parseEther('0.01'),
gasPrice: ethers.utils.parseUnits('100', 'gwei'),
gasLimit: 21000
};
const signedBundle = await flashbotsProvider.signBundle([
{ signer: wallet, transaction: tx }
]);
const response = await flashbotsProvider.sendBundle(signedBundle, blockNumber + 1);
console.log('Bundle response:', response.bundleHash);
}
main();
Why this works: Flashbots bundles let you atomically submit multiple TXs that miners can include without public front-running risks. The provider supports bundle simulation and mining eligibility checks, which are invaluable during development.
Tip: Always test on Goerli or other testnets before mainnet, and watch for your flashbots provider version. APIs can change with bundle handling improvements.
For more on Flashbots bundle types and advanced submission, check out flashbots bundle typescript example.
Depending on your stack, MEV bots can be efficiently written in TS or Python.
Backrunning is when your bot reacts to a detected on-chain trade to capture arbitrage or liquidation profit. Here’s an extremely simplified Python pseudo-code snippet:
from web3 import Web3
w3 = Web3(Web3.HTTPProvider('https://goerli.infura.io/v3/YOUR_INFURA_KEY'))
def backrun_block(block):
for tx in block['transactions']:
# Simplified: detect AMM swaps that creates arbitrage
if is_profitable_arbitrage(tx):
tx_hash = send_backrun_tx()
print(f'Backrun sent with tx hash: {tx_hash}')
def main():
block_number = w3.eth.blockNumber
block = w3.eth.getBlock(block_number, full_transactions=True)
backrun_block(block)
if __name__ == '__main__':
main()
This sketch omits the complexity of building signed bundles and nonce management, but combining it with Flashbots bundle submission tools (flashbots-py or direct RPC) makes it viable.
Sandwich bots are highly contentious but technically intriguing. They identify a large pending swap, front-run it with a buy, then back-run with a sell.
In TypeScript, the flow typically involves:
eth_getTransactionPool or public RPC subscriptionsYou’ll want to carefully bucket your gas price and remember sandwich bots carry sandwich risk: your front-run tx might fail leaving you stuck.
The Flashbots example above is a good base to add custom logic for mempool monitoring and bundle composition beyond the simple 1-tx bundle.
If you prefer Rust, Artemis offers a promising framework for building MEV searchers with modular strategy components.
A minimal example from Artemis docs (pseudo-code):
use artemis::{Collector, Strategy, Executor};
struct MyStrategy;
impl Strategy for MyStrategy {
fn on_new_block(&self, block_data: &BlockData) {
// Detection and MEV logic
}
}
fn main() {
let collector = Collector::new("RPC_URL");
let strategy = MyStrategy;
let executor = Executor::new("Flashbots_ENDPOINT");
collector.run(strategy, executor);
}
Artemis is still early-stage — be cautious of API changes and incomplete documentation. But for Rustaceans wanting tighter memory control and concurrency, it’s worth exploring.
On Solana, Jito provides MEV tooling optimized for low-latency bundle submission and block construction.
Jito also implements a tip account system that allows searchers to incentivize block producers directly. Setting up a Jito tip account requires wallet setup and funding before you can reliably submit bundles.
// Rust pseudo-code for Jito bundle
let bundle = Bundle::from_txs(vec![txn1, txn2]);
client.submit_bundle(bundle).await?;
Backrunning on Solana requires efficient mempool monitoring (highly concurrent) and submitting bundles with low-latency RPC.
Tools like solana-client and Jito's SDK support the pattern. Integration typically involves:
For Python developers, jito block engine searcher python libraries simplify the process with abstractions over RPC and bundle serialization.
| Bot Type | Chain | Language | Key Points |
|---|---|---|---|
| Flashbots Searcher | Ethereum | TypeScript | Private bundle submission, gas price control |
| Backrunning Arbitrage Bot | Ethereum/Solana | Python / Rust | Requires fast block data parsing, profitable trade detection |
| Sandwich Bot | Ethereum | TypeScript | Mempool monitoring, high risk of failed front-run TXs |
| Liquidation Bot | Ethereum | Python | Monitoring lending protocols, event-triggered tx submission |
| Artemis MEV Framework | Ethereum | Rust | Modular strategy, collector, executor pattern |
| Jito MEV Searcher | Solana | Rust/Python | Low-latency RPC, tip account incentivization |
These bot archetypes have varying operational complexity — flash loan arbitrage bots, for example, depend heavily on atomicity via bundles.
Here are some real-world gotchas when building MEV bots:
Often, I've found that session keys with spending limits improve safety by restricting potential loss if keys leak or get compromised.
For more troubleshooting tips on trading bots, see our trading-bot-troubleshooting page.
MEV bot development is a specialized craft requiring understanding of on-chain mechanics, private transaction submission, and bot architecture. Flashbots for Ethereum and Jito for Solana provide solid starting points — but mastering them takes iteration.
I encourage you to start by shipping a simple Flashbots bundle using the TypeScript example above, then progressively add mempool monitoring, arbitrage detection, and backrun logic. Exploring Artemis gives Rust developers a robust framework to craft modular MEV strategies.
Finally, always design with security first: private key safety, spending limits, and cautious approval management protect your capital.
Check out related tutorials on Freqtrade for integrating AI-driven trading with MEV strategies, or agent-payment-protocols-x402 for agent wallet security when automating these bots.
Happy coding — and may your bundles get included!