How to Architect a MEV-Aware Transaction Router

A MEV-aware transaction router is a system that sits between a user and the blockchain, intelligently routing transactions to protect against value extraction by searchers and validators. Unlike a standard wallet or RPC endpoint, it analyzes transaction intent, simulates potential outcomes, and selects the execution path that maximizes user value—be it through better prices, reduced slippage, or direct MEV protection. The primary goal is to turn a user's transaction from MEV prey into a neutral or even advantageous interaction with the network's economic layer.

Architecting such a system requires understanding the MEV supply chain: users, wallets, searchers, builders, and validators. Your router must intercept transactions before they enter the public mempool, where they are vulnerable to frontrunning and sandwich attacks. This is typically done via a private transaction relay or a direct submission to a trusted builder. Key components include a simulation engine to test bundle permutations, a market for block space (like Flashbots Protect or a private RPC), and a decision engine that applies routing rules based on transaction type, gas parameters, and real-time network conditions.

For a practical example, consider routing a simple token swap. A naive router might send it directly to a public mempool. A MEV-aware router would first simulate the swap on multiple DEXs and across different liquidity pools via aggregators like 1inch or CowSwap. It would then check if the transaction creates profitable MEV opportunities for searchers by analyzing slippage and liquidity depth. Finally, it would package the transaction—potentially with a competitive fee or as part of a backrun bundle that captures some value for the user—and submit it through a private channel to a block builder.

The technical stack often involves Node.js or Python for the orchestration layer, Ethereum execution clients (Geth, Erigon) for local simulation, and RPC connections to services like Flashbots. Critical logic resides in the simulation; you must accurately predict state changes and gas costs. Tools like Tenderly's simulation API or Foundry's forge script can be integrated for this purpose. The router must also manage private keys securely for transaction signing, often using a hardware security module (HSM) or a dedicated signer service like Web3Signer.

Ultimately, success is measured by end-of-block state improvement: did the user get a better net outcome than a standard broadcast? This requires continuous monitoring and adaptation. By architecting with MEV in mind from the start, you build a system that doesn't just execute transactions but actively defends and optimizes user value in the adversarial environment of a public blockchain.

Architecting a MEV-aware transaction router requires a multi-layered technical foundation. You must understand the Ethereum Virtual Machine (EVM) execution model, including gas, opcodes, and state transitions. Familiarity with smart contract development in Solidity or Vyper is essential for analyzing the contracts your router will interact with. Crucially, you need to grasp the mechanics of the mempool—the public waiting area for pending transactions—where searchers and bots compete to identify profitable opportunities through techniques like frontrunning and backrunning.

Your development environment must be equipped with the right tools. A Node.js or Python setup is standard. You will need an Ethereum execution client (like Geth or Erigon) for accessing the mempool and broadcasting transactions, or a reliable RPC provider with access to transaction streams. Libraries such as ethers.js or web3.py are necessary for constructing, signing, and sending transactions. For simulating transactions and analyzing potential MEV, you'll use tools like Tenderly, Foundry's cast, or Ethereum execution APIs.

A deep, practical understanding of transaction lifecycle is non-negotiable. This includes knowing how a signed transaction propagates through the peer-to-peer network, how it gets included in a block by a validator, and the role of transaction fees (base fee, priority fee, and max fee). You must be able to calculate gas costs accurately and understand how EIP-1559 has changed fee market dynamics. This knowledge allows you to model the economic incentives for both users and block builders.

Finally, you must study existing MEV infrastructure to know what you're routing around or through. Analyze the strategies of searchers who bundle transactions and the builders who construct blocks. Understand the role of Flashbots and the mev-boost protocol in the post-Merge PBS (Proposer-Builder Separation) landscape. Reviewing real-world MEV bundles on explorers like EigenPhi or Flashbots Explorer provides concrete examples of arbitrage, liquidations, and other common strategies.

A MEV-aware transaction router is a client-side system that submits user transactions while minimizing their exposure to harmful MEV extraction like frontrunning and sandwich attacks. Its core architectural principle is to avoid the public mempool whenever possible. Standard transactions broadcast to a public JSON-RPC endpoint are visible to searchers and bots who can analyze and exploit the pending transaction's intent. The router's primary defense is to use private transaction relays (e.g., Flashbots Protect, bloXroute, Taichi Network) or direct mempool submission to trusted builders/validators, bypassing the public gossip network entirely.

The architecture must also manage transaction simulation and gas optimization in an MEV-sensitive context. Before signing, the router should simulate the transaction's execution path using a local node or a service like Tenderly. This checks for failures and estimates precise gas, preventing the need for broadcasted gas auctions that reveal intent. For complex operations—like a multi-step DeFi swap—the router can use a gas estimation oracle and set a priority fee cap to avoid being outbid by predatory bots in the public mempool, a common trigger for sandwich attacks.

For advanced protection, integrate with SUAVE (Single Unified Auction for Value Expression), a dedicated decentralized block-building network. A SUAVE-compatible router can submit its transaction to a SUAVE relayer, which includes it in a cryptographically committed bundle sent directly to builders. This hides transaction content until block inclusion. Alternatively, use application-specific solutions like CowSwap's CoW Protocol which employs batch auctions and solver competition to settle trades without exposing them to on-chain frontrunning, effectively acting as a MEV-aware router for token swaps.

Implementing these concepts requires specific code patterns. Instead of using eth_sendRawTransaction, your router's submission logic should call a private RPC endpoint. For example, to send via Flashbots Protect on Ethereum mainnet, you would target https://rpc.flashbots.net or use the Flashbots eth_sendPrivateTransaction RPC method. Your signing client (e.g., Ethers.js, Viem) must be configured with this provider. Crucially, always attach a maxPriorityFeePerGas and simulate the TX locally first to avoid reverts that would force a public rebroadcast.

Finally, architect for user choice and fallback mechanisms. The router should allow users to select a preferred relay or opt for speed over cost protection. It must also have a robust fallback strategy: if a private transaction is not included within a set number of blocks, the system needs policies for cancellation (sending a zero-gas replacement TX with same nonce) or, as a last resort, controlled public broadcasting. Monitoring tools like EigenPhi or Mev-inspect can be integrated to audit the router's performance and confirm it successfully shielded transactions from extractable value.

A MEV-aware transaction router sits between a user's wallet and the blockchain's transaction pool (mempool). Its primary function is to evaluate and optimize transaction execution in a landscape where searchers and validators compete for profit. The core architecture consists of three logical layers: the Client Interface for receiving user intents, the Strategy & Simulation Engine for analyzing transaction outcomes, and the Broadcast & Relay Network for secure submission. This separation allows for modular upgrades to individual components, such as integrating new block builders or simulation providers.

The Strategy Engine is the intelligence core. It must simulate a transaction's execution across multiple potential paths. This involves using services like Tenderly or Foundry's cast to perform local state simulations, checking for common MEV pitfalls like sandwich attacks on DEX swaps. For example, a router might simulate a Uniswap V3 swap, check the resulting price impact against a threshold, and reroute the trade through a private mempool like Flashbots Protect if the slippage is deemed exploitable. The engine consults an internal MEV Opportunity Database, which tracks known predatory bots and common attack patterns.

Integrating with private transaction relays is a critical defensive component. Submitting transactions directly to the public mempool exposes them to generalized front-running bots. Architecturally, the router should maintain connections to multiple relays—such as Flashbots, bloXroute, and Eden—and implement a relay selector. This selector evaluates factors like inclusion guarantees, cost, and latency. The submission process often uses the eth_sendPrivateTransaction RPC method. The system must also handle fallback logic, reverting to public broadcast if private relays fail, while implementing delays to avoid instant mempool exposure.

To be truly effective, the architecture must include a Post-Execution Analysis Module. After a transaction is mined, this module analyzes the block it landed in, comparing the actual execution outcome (final token amounts, gas used) against the initial simulations. It looks for indicators of negative MEV, such as a profitable sandwich transaction appearing in the same block. This data feeds back into the Strategy Engine's database, creating a closed-loop learning system that improves future routing decisions. Tools like EigenPhi or custom indexers can be integrated here to parse block data.

Finally, consider the fee management system. A MEV-aware router must dynamically adjust gas prices and priority fees (maxPriorityFeePerGas). It should pull real-time fee estimates from oracles like Etherscan Gas Tracker or Blocknative, and potentially implement Gas Auction Logic when competing in private channels. The architecture should allow strategies to bid aggressively for block space when arbitrage opportunities are detected for the user's benefit, while using conservative fees for simple transfers. All fee logic must be transparent to the user, with clear pre- and post-execution reporting.

At its core, a MEV-aware transaction router is a decision engine that simulates, evaluates, and selects the best execution path for a given trade or operation. Unlike a simple aggregator that only finds the best price, a sophisticated router must consider transaction ordering dependencies, liquidity fragmentation, and the potential for sandwich attacks or frontrunning. The primary goal is to maximize user value—final output minus fees and slippage—while minimizing exposure to harmful MEV. This requires real-time analysis of the mempool, pending transactions, and on-chain state across multiple blockchains and layer-2 networks.

The architecture typically consists of three key components: a pathfinder, a simulator, and an executor. The pathfinder, often using graph-based algorithms, discovers all possible routes for a swap (e.g., ETH -> USDC via Uniswap V3, or ETH -> WETH -> USDC via a Balancer pool). The simulator then runs local or forked EVM simulations to estimate gas costs, slippage, and the final output amount for each route. Crucially, it must also simulate the impact of pending transactions visible in the public mempool to assess MEV risk. The executor bundles the chosen route into a transaction, potentially using privacy services like Flashbots Protect or a private RPC to mitigate frontrunning.

Effective simulation is the most critical technical challenge. You cannot rely on simple eth_call RPCs, as they don't account for state changes from pending transactions. Instead, you need a local EVM fork using tools like Ganache, Hardhat, or Anvil. Your simulator must replay the last block, apply all pending transactions from a connected mempool stream (e.g., from Bloxroute, Blocknative), and then test your candidate transaction. This reveals if your trade would be sandwiched. For advanced protection, consider bundle simulation where your transaction is sent as a private bundle to a block builder via the Flashbots eth_sendBundle API, ensuring atomic execution.

Here's a simplified conceptual flow in pseudocode:

codeCopy
function routeTransaction(userIntent) {
  // 1. Find all possible paths
  let routes = pathfinder.findRoutes(userIntent);
  
  // 2. Simulate each route against current mempool state
  let evaluatedRoutes = [];
  for (route of routes) {
    let simulationResult = simulator.forkAndSimulate(route, pendingTxs);
    let mevRiskScore = analyzer.calculateMEVRisk(simulationResult);
    let netValue = simulationResult.output - simulationResult.gasCost;
    evaluatedRoutes.push({route, netValue, mevRiskScore});
  }
  
  // 3. Select optimal route balancing value and safety
  let optimalRoute = optimizer.select(evaluatedRoutes);
  
  // 4. Execute via private channel or public mempool
  return executor.send(optimalRoute, 'private');
}

Integrating with private transaction relays is essential for production systems. Services like Flashbots Protect, Bloxroute's Protected RPC, and Eden Network provide APIs to submit transactions directly to block builders, bypassing the public mempool. This prevents frontrunning but introduces new considerations: relay latency, cost (priority fees paid to builders), and compatibility across different chains (Ethereum, Arbitrum, Optimism). Your router must dynamically choose between public and private submission based on the transaction size, urgency, and the simulated MEV risk. For large trades (>0.5% of pool liquidity), private submission is almost always necessary.

Finally, continuous monitoring and adaptation are required. The MEV landscape evolves rapidly with new attack vectors (e.g., time-bandit attacks) and mitigation techniques. Implement logging to track simulation accuracy versus actual on-chain outcomes. Use this data to refine your risk models. Open-source projects like the Ethereum Execution Client Specification and MEV research from organizations like Flashbots and ChainSecurity provide vital resources. By architecting your router with modularity—separating pathfinding, simulation, and execution—you can adapt components independently as the ecosystem changes, ensuring your users receive optimal, secure execution over time.

Architecting a MEV-aware transaction router is not a one-time task but an ongoing process of adaptation. The strategies discussed—from using private RPCs like Flashbots Protect or BloXroute for transaction privacy, to implementing dynamic fee estimation with services like EigenPhi for simulation—form a defensive foundation. However, the MEV landscape evolves rapidly with new extraction techniques and protocol upgrades. Your router's logic must be modular to allow for quick integration of new data sources, such as real-time mempool streamers from Blocknative or Chainbound, and new protection mechanisms as they emerge.

For developers ready to implement, start by instrumenting your existing transaction submission flow. Use the eth_sendRawTransaction method with a privacy-focused RPC endpoint as your baseline. Then, layer in simulation: before broadcasting, run a local fork using Foundry or Hardhat to test transaction outcomes against known sandwich attack patterns. Consider batching user transactions where possible to dilute the value of any single transaction to searchers. Open-source libraries like mev-inspect-py and mev-tools provide valuable starting points for analysis and detection logic.

The ultimate goal is to create a system that maximizes user execution quality—a combination of successful execution, minimal slippage, and fair cost. This requires measuring performance. Track metrics like inclusion time, effective gas price paid versus estimated, and the frequency of transactions being frontrun or sandwiched. Tools like the Ethereum Execution API's eth_createAccessList can help optimize gas costs post-design, further improving user outcomes. By treating MEV not just as a threat but as a measurable component of transaction execution, you can build a router that consistently delivers superior results.

Looking forward, the infrastructure for MEV-aware routing will become more sophisticated. Shared sequencers from rollups like Arbitrum and Optimism aim to democratize block building, while protocols like SUAVE envision a decentralized block building market. Staying informed through research forums like the Flashbots Research repository and EthResearch is crucial. The next evolution of your router may involve participating in these new markets or integrating intent-based transaction standards, where users specify desired outcomes rather than precise execution paths, shifting the MEV management burden entirely to the network layer.