Building MEV Bots: Rust vs Python vs JavaScript — Which Language Wins?

Published February 28, 2026 · Updated March 7, 2026 · By JaredFromSubway

Rust: Maximum Performance, Maximum Complexity

Rust is the language of choice for competitive MEV searchers who need every microsecond of advantage. It compiles to native machine code with zero-cost abstractions, no garbage collector, and deterministic memory management. For a MEV bot that needs to process thousands of mempool transactions per second and respond in sub-millisecond timeframes, Rust is unmatched.

The Ethereum Rust ecosystem has matured rapidly. Alloy, developed by the same team behind Foundry, is now the standard Rust library for Ethereum interaction. It provides type-safe ABI encoding and decoding, transaction construction, and RPC client functionality. Reth, the Rust-based Ethereum execution client, gives searchers who run their own nodes a significant latency advantage because they can hook directly into the transaction pool at the code level rather than relying on WebSocket subscriptions.

Key strengths for MEV:

• Sub-millisecond mempool transaction decoding and ABI parsing
• Zero-copy deserialization of RLP-encoded data with alloy-rlp
• Fearless concurrency with async runtimes like Tokio for parallel mempool monitoring
• Direct integration with Reth for node-level transaction pool access
• Memory safety guarantees prevent crashes during high-throughput operation
• No garbage collection pauses that could cause missed opportunities

Drawbacks: Rust has a notoriously steep learning curve. The borrow checker, lifetimes, and trait system require significant investment to master. Prototyping a new MEV strategy in Rust takes 3-5x longer than in Python. Compile times are also substantial, slowing the iteration cycle during strategy development.

Python: Fastest Prototyping, Slowest Execution

Python dominates the research and prototyping phase of MEV development. Its expressiveness, vast data science ecosystem, and the web3.py library make it the fastest path from idea to working proof of concept. You can connect to a node, subscribe to pending transactions, decode calldata, and simulate a sandwich attack in under 100 lines of Python.

For backtesting MEV strategies against historical data, Python is unrivaled. Libraries like pandas and numpy allow you to analyze millions of historical transactions, calculate profitability across different market conditions, and optimize strategy parameters. Jupyter notebooks provide an interactive environment where you can iterate on strategy logic, visualize results, and share findings with your team.

Key strengths for MEV:

• Rapid prototyping: test a new strategy idea in hours, not days
• web3.py provides comprehensive Ethereum interaction with minimal boilerplate
• Excellent for backtesting with pandas, numpy, and matplotlib
• Rich ecosystem for data analysis and machine learning (strategy optimization)
• Interactive development with Jupyter notebooks
• Lowest barrier to entry for developers new to blockchain

Drawbacks: Python is fundamentally too slow for production MEV in competitive environments. The Global Interpreter Lock (GIL) prevents true parallelism. Dynamic typing introduces runtime overhead on every operation. Garbage collection introduces unpredictable pauses. A Python bot monitoring the mempool will consistently lose to Rust and even JavaScript bots when competing for the same opportunity. Python MEV bots can still be profitable on less competitive chains or for strategies where speed is not the primary advantage, but on Ethereum mainnet in 2026, Python alone is not enough.

JavaScript/TypeScript: The Middle Ground

JavaScript and TypeScript occupy a practical middle ground between Rust's raw performance and Python's development speed. The V8 engine's JIT compilation delivers execution speeds that are 5-10x faster than Python for most operations, while TypeScript's type system catches errors at compile time without imposing Rust-level complexity.

The JavaScript Ethereum ecosystem is the largest and most battle-tested. ethers.js has been the standard for years, and viem has emerged as a modern, TypeScript-first alternative with better performance and stricter typing. Both libraries offer comprehensive transaction decoding, contract interaction, and wallet management. The Flashbots bundle library has first-class JavaScript support, making bundle submission straightforward.

Key strengths for MEV:

• V8 JIT compilation provides competitive execution speed
• Largest Ethereum library ecosystem: ethers.js, viem, Flashbots SDK
• TypeScript catches bugs at compile time while maintaining development velocity
• Non-blocking async I/O is well-suited for WebSocket mempool subscriptions
• Shared language with front-end dashboards and monitoring tools
• Most MEV tutorials and open-source bots are written in JavaScript

Drawbacks: JavaScript cannot match Rust for raw computation speed. The garbage collector, while more predictable than Python's, still introduces occasional pauses. Memory usage is higher than Rust. For the most latency-sensitive operations like mempool decoding and bundle construction, JavaScript is measurably slower. However, for many profitable MEV strategies, JavaScript is fast enough.

Performance Benchmarks: Mempool Decode and Bundle Construction

The following benchmarks represent typical performance on a modern server (AMD EPYC 7763, 64GB RAM) for common MEV bot operations. These numbers come from internal testing by the JaredFromSubway engineering team and reflect real-world conditions rather than synthetic benchmarks.

The numbers tell a clear story. Rust is roughly 8x faster than JavaScript and 60x faster than Python for end-to-end MEV operations. In a competitive environment where multiple searchers are targeting the same opportunity, 2.1ms versus 18.4ms is often the difference between landing your bundle and losing the auction. Python's 127.6ms is simply not viable for on-chain competition on Ethereum mainnet.

Real-World Architecture: Use Each Language Where It Excels

The most successful MEV operations do not use a single language. They use a polyglot architecture that leverages each language's strengths. Here is the pattern that the top searchers, including JaredFromSubway, converge on:

Rust for the hot path: Mempool monitoring, transaction decoding, opportunity detection, EVM simulation (using revm), bundle construction, and submission to block builders. This is the latency-critical pipeline where every millisecond counts. Rust handles this with sub-millisecond precision.

Python for analytics and backtesting: Strategy research, historical data analysis, profitability modeling, and parameter optimization. Python scripts run offline against archived mempool data and on-chain state to identify new strategies and tune existing ones. This work is not latency-sensitive and benefits enormously from Python's data science ecosystem.

TypeScript for dashboards and monitoring: Real-time dashboards showing bot performance, P&L tracking, alert systems, and operational tooling. TypeScript with Next.js or similar frameworks provides a fast development cycle for web interfaces that the team uses to monitor operations. This is also where crypto trading bot configuration interfaces typically live.

What JaredFromSubway's Stack Looks Like

The original JaredFromSubway bot, which became famous in 2023 for extracting millions in MEV from Ethereum, was built primarily with TypeScript and Solidity. The bot used ethers.js (later migrated to viem) for mempool monitoring and transaction decoding, with the actual sandwich logic executed in custom Solidity smart contracts deployed on-chain. The contract-level execution meant that the critical swap operations happened at EVM speed regardless of the off-chain language.

This architecture is important to understand: the off-chain bot handles detection and submission, but the on-chain contract handles execution. A well-optimized Solidity contract with inline assembly for gas optimization is the same regardless of whether the off-chain component is Rust, TypeScript, or Python. The language choice primarily affects the detection and submission latency — the time between seeing a transaction in the mempool and getting your bundle to a block builder.

As competition intensified through 2024-2026, many JaredFromSubway-style operations have migrated their hot paths to Rust while keeping TypeScript for tooling and Python for research. The trend is clear: the searchers who win consistently are the ones running Rust on bare metal with co-located nodes and direct builder connections.

Framework Comparison: Foundry vs Hardhat vs Brownie

Beyond the bot itself, the development framework you use for smart contract development and testing significantly impacts your workflow. Here is how the three major frameworks compare for MEV-specific development:

Foundry (Rust)

Foundry has become the default framework for serious MEV developers. Written in Rust, it compiles and tests Solidity contracts dramatically faster than alternatives. Its flagship tool, forge, can run thousands of fuzz tests in seconds. The built-in cast CLI lets you interact with on-chain contracts, decode calldata, and send transactions directly from the terminal. Foundry's trace output is invaluable for debugging failed MEV transactions — you can see exactly which opcode caused a revert and why your sandwich simulation diverged from on-chain execution.

Hardhat (JavaScript/TypeScript)

Hardhat remains the most popular Solidity development framework by user count. Its plugin ecosystem is massive, with tools for gas reporting, contract verification, and deployment management. For MEV developers, Hardhat's forking mode is particularly useful — you can fork mainnet state at any block and simulate your MEV transaction against the exact state that existed when the opportunity appeared. The TypeScript integration means your test scripts can share code with your bot's off-chain logic.

Brownie (Python)

Brownie was once popular for Python-based Solidity development but has been largely superseded by Ape Framework (also Python). Both provide Pythonic interfaces for contract deployment, testing, and interaction. They integrate well with Python-based MEV research workflows — you can backtest a strategy in a Jupyter notebook using the same contract interfaces you use in production. However, the Python framework ecosystem has not kept pace with Foundry's speed improvements. Most teams that started with Brownie have migrated to Foundry for contract development while keeping Python scripts for analytics.

When to Use Each Language

The right language depends on what stage of MEV development you are in and what you are optimizing for. Here is a decision framework:

The Trend: Rust Dominance in 2025-2026 for Competitive MEV

The data is unambiguous. In 2023, most MEV bots were written in JavaScript or Python. By mid-2025, the majority of profitable searchers on Ethereum mainnet had migrated to Rust. This shift was driven by three converging factors.

First, the rise of Reth gave Rust developers a native Ethereum client they could modify and extend. Running a custom Reth node with direct transaction pool hooks eliminates the WebSocket latency that JavaScript and Python bots rely on. Second, Alloy matured into a production-grade Ethereum library that matches or exceeds ethers.js in functionality while being dramatically faster. Third, the MEV supply chain itself became more competitive: block builder auctions, proposer-builder separation, and order flow auctions all increased the premium on speed.

JaredFromSubway has observed this trend firsthand. The bots that consistently win builder auctions and land profitable bundles in 2026 are overwhelmingly Rust-based. This does not mean Python and JavaScript are irrelevant — they remain essential for the non-latency-critical parts of an MEV operation. But the hot path, where money is made or lost in microseconds, belongs to Rust.

Frequently Asked Questions