Setting Up a Framework for AI-Suggested Gas Optimization

Gas optimization is a critical skill for Web3 developers, directly impacting user experience and protocol efficiency. On Ethereum and other EVM chains, every computational step costs gas, a unit of transaction fee. Inefficient smart contracts can lead to prohibitively high costs, failed transactions, and poor user retention. While manual optimization techniques exist—like using uint256 over smaller types or minimizing storage writes—they require deep expertise and are reactive. An AI-suggested framework shifts this paradigm to proactive, data-driven optimization.

The core of this framework involves analyzing transaction data, simulating gas costs under different conditions, and using machine learning models to suggest specific code improvements. You'll need to interact with several key components: a blockchain node (or RPC provider like Alchemy or Infura) to fetch data, a tool for simulating transactions (like Hardhat or Foundry's forge), and a system to track historical gas prices. The AI component typically involves training a model on datasets of optimized vs. non-optimized contract transactions to learn patterns.

For example, a basic system flow might be: 1) Monitor pending transactions in the mempool, 2) Decode their calldata to understand the function being called, 3) Use a local fork to simulate the transaction with alternative, more gas-efficient parameters or logic, 4) Compare the gas usage, and 5) If a significant saving is found, suggest it to the user or developer. This requires writing scripts that can programmatically interact with an Ethereum node using libraries like ethers.js or web3.py.

Implementing this framework provides tangible benefits. It can automatically identify common inefficiencies such as redundant checks, expensive loops in Solidity, or suboptimal data structures. By integrating this into a development pipeline, teams can catch costly patterns before deployment. Furthermore, for end-users, a wallet-integrated version could suggest optimal gas price settings (max fee and priority fee) based on current network congestion predictions, reducing overpayment.

This guide will walk through building a functional proof-of-concept. We'll set up a Node.js environment, connect to a blockchain node, write simulation scripts with Hardhat, and implement a simple rule-based suggestion engine as a precursor to a full ML model. The final code will be able to analyze a provided contract address and transaction, run simulations, and output a human-readable optimization report with estimated gas savings.

Gas optimization requires a structured approach to measure and test changes. You'll need a development environment with tools for transaction simulation, gas profiling, and historical data analysis. Essential components include a local Ethereum node (like Geth or Erigon) or a node provider API (such as Alchemy or Infura), a testing framework like Hardhat or Foundry, and a method to collect on-chain transaction data. This setup allows you to replay real transactions in a controlled sandbox to benchmark gas costs before deploying any optimization.

Your framework must integrate with the blockchain to fetch live state. Use the eth_call RPC method to simulate transactions without broadcasting them. For profiling, tools like Hardhat's console.log for gas or Foundry's forge test --gas-report are critical. You should also establish a pipeline to collect data from public mempools or services like Etherscan to analyze common transaction patterns and failure modes. This data becomes the training ground for identifying optimization opportunities.

Finally, define clear metrics for success. Track gas used, effective gas price, and total cost in USD per transaction type. Compare optimized versions against baselines under identical network conditions (same block number, state). This reproducible testing environment is the prerequisite for any reliable AI or algorithmic suggestion system, ensuring recommendations are based on empirical evidence rather than theoretical estimates.

An AI-suggested gas optimization framework operates as a client-side middleware layer. It sits between a user's wallet (like MetaMask) and the Ethereum network, intercepting transaction requests before they are signed and broadcast. The core components are a prediction engine that uses machine learning models to estimate optimal gas parameters, a data aggregator that collects real-time on-chain and mempool data, and a suggestion API that delivers recommendations to the user's interface. This architecture prioritizes privacy and security, as sensitive transaction data never needs to leave the user's device for basic analysis.

The prediction engine is the system's brain, typically built using models trained on historical blockchain data. It analyzes patterns in base fee trends, priority fee (tip) auctions, and network congestion from sources like the Beacon Chain and mempool streams. For example, a model might be trained on months of EIP-1559 data to predict the base fee for the next block with high accuracy. The engine can be implemented as a lightweight, local model (e.g., using ONNX runtime) or query a remote, high-performance API, depending on the trade-off between latency, cost, and privacy requirements.

Data aggregation is critical for accurate suggestions. The system must ingest live feeds from multiple sources: the execution layer (current base fee, pending transactions), the consensus layer (validator activity, slot times), and alternative RPC providers for redundancy. Services like the Ethereum Beacon Chain API, Blocknative's Mempool Explorer, and decentralized oracle networks provide this data. The aggregator normalizes and caches this information, ensuring the prediction engine has a low-latency, consistent view of network state to make its calculations.

Integration with user wallets happens through the suggestion API. For browser extensions, this involves injecting a script that listens for transaction creation events. When a eth_sendTransaction call is intercepted, the system's client fetches a gas suggestion from the local or remote API. The suggestion, including a recommended maxFeePerGas and maxPriorityFeePerGas, is then displayed to the user for approval. Developers can implement this using libraries like EIP-1193 for provider interaction, ensuring compatibility with most Ethereum wallets.

A robust architecture must handle edge cases and failures gracefully. This includes implementing fallback mechanisms to default gas estimation RPC calls if the AI model is unavailable, setting safety caps on suggested fees to prevent overpayment, and providing transparent reasoning for each suggestion (e.g., "High congestion detected, increasing tip by 20%"). The system should also be chain-aware, adapting its models and data sources for different EVM-compatible networks like Arbitrum, Optimism, or Polygon, which have distinct fee market dynamics.

The first step in building an AI-powered gas optimization tool is to create a code analysis module. This module is responsible for parsing, understanding, and extracting meaningful data from Solidity smart contracts. We'll use the Solidity compiler (solc) to generate an Abstract Syntax Tree (AST), which provides a structured, machine-readable representation of the source code. The AST allows us to programmatically traverse the contract's functions, variables, and control structures without executing the code.

To begin, set up a Node.js project and install the required dependencies. You'll need the solc package for compilation and a library like @solidity-parser/parser for more granular AST traversal. The core function of this module is to compile a given Solidity file and output its AST. For example, using solc version 0.8.20, you can compile with the --ast-compact-json flag or use the JavaScript API to get a JSON representation of the AST, which includes nodes for every contract, function, statement, and expression.

Once you have the AST, you need to write traversal logic to identify specific patterns. This involves searching for common gas-intensive constructs such as: loops with unbounded iterations, expensive storage operations inside loops, redundant state variable reads, and inefficient data types (e.g., uint256 vs. uint8 in storage). Your analysis module should tag these patterns with their location (file, line number) and a severity score. This structured output becomes the dataset for the subsequent AI suggestion engine.

A critical component is context awareness. Not all patterns are optimizable in every context. For instance, a memory array in a view function is less critical than a storage array in a frequently called transaction. Your module should annotate each finding with contextual metadata: whether it's inside a loop, its visibility (public/external), and its state mutability (view/pure/nonpayable). This context is vital for the AI to generate relevant, actionable suggestions rather than generic advice.

Finally, package this analysis into a reusable function or class. The module's API should accept a Solidity source string or file path and return a JSON object containing the list of potential optimizations. This modular approach allows you to test the analysis independently and easily integrate it with the next stages: the AI model integration and the suggestion formatting module. Ensure your code handles compiler errors gracefully and supports multiple Solidity pragma versions for broader compatibility.

The suggestion engine's primary function is to ingest raw transaction data—such as the target contract address, function signature, calldata, and current network conditions—and output a prioritized list of optimization strategies. We implement this as a rule-based system, where each rule is a discrete function that checks for a specific, common gas inefficiency pattern. For example, one rule might scan for redundant storage writes within a loop, while another checks if expensive on-chain computations could be moved off-chain. The engine evaluates the transaction against all active rules, collecting any that trigger a match.

Each rule must return a standardized Suggestion object. This object contains a severity score (e.g., low, medium, high), a clear description of the issue, a code snippet illustrating the inefficient pattern, and a corrected code snippet showing the optimized alternative. For instance, a rule for unchecked math might return: { severity: 'medium', title: 'Use Unchecked Arithmetic', description: 'SafeMath is not required for this operation which cannot overflow.', example: 'a = a + b;', optimizedExample: 'unchecked { a = a + b; }' }. This structured output allows the frontend to display consistent, actionable feedback.

To make the engine context-aware, we integrate real-time gas price data from providers like Etherscan or a Gas Station API. A suggestion's priority can be adjusted based on the current baseFee and priorityFee. An optimization saving 10,000 gas is far more valuable when gas prices are 100 gwei than when they are 10 gwei. The engine can calculate the potential fee savings in USD by combining the gas delta with the current gas price and ETH/USD rate, presenting users with a tangible cost-benefit analysis for each suggestion.

Finally, the engine needs a scoring and ranking system. A simple approach is to assign a base weight to each suggestion type and then multiply it by the current gas price multiplier. The suggestions are then sorted by this final score, ensuring the most impactful optimizations are presented first. The complete engine, a collection of these modular rules fed with live data, transforms raw transaction analysis into a prioritized, contextualized list of improvements a developer can immediately implement.

A gas benchmarking framework provides the quantitative foundation for AI-driven optimization. Its primary function is to measure and compare the gas cost of executing a smart contract function before and after applying AI-suggested modifications. This requires a deterministic testing environment where you can execute the same transaction multiple times with consistent state. Tools like Hardhat, Foundry, and dedicated libraries such as benchmark-rs for Solana programs are essential for creating these isolated, repeatable test scenarios.

The core of the framework involves writing specific test cases that simulate key user interactions with your contract. For each function you wish to optimize, you should create a benchmark test that:

• Deploys a fresh contract instance to ensure a clean state.
• Executes the target function with a representative set of input data.
• Records the transaction's gas consumption using the provider's RPC method (e.g., eth_estimateGas) or the testing framework's utilities. This process is automated to run the original (baseline) and optimized (candidate) versions sequentially, outputting a clear delta.

To ensure benchmarks are meaningful, your test data must reflect real-world usage. This includes testing with:

• Edge cases (minimum/maximum values, empty arrays).
• Average-case scenarios with typical parameter sizes.
• High-frequency operations that will be called repeatedly. For example, benchmarking a Uniswap V3 swap function should use different liquidity tiers and tick ranges. The results should be logged in a structured format (like JSON or CSV) for easy analysis and integration with the AI feedback loop.

Integrating this framework into a CI/CD pipeline automates the validation of every AI-suggested change. A script can be configured to:

1. Run the benchmark suite on the main branch code to establish a baseline.
2. Apply the AI-proposed code patch.
3. Re-run the benchmarks on the modified code.
4. Compare results and fail the build if gas usage increases beyond a defined tolerance (e.g., 1-2%), or if the optimization is negligible (e.g., less than 0.5%). This creates a gatekeeping mechanism that ensures only genuinely beneficial optimizations are merged.

Finally, the benchmark data serves as critical feedback for refining the AI's suggestions. By logging which code patterns consistently lead to gas reductions versus those that don't, you can curate a dataset to fine-tune the underlying model. This creates a virtuous cycle: the AI proposes changes, the benchmark validates them, and the results train the AI to make better future proposals. Over time, this system learns the specific gas cost patterns of your protocol's architecture and EVM version.

A security regression occurs when an optimization inadvertently weakens a smart contract's security posture. Common risks include reducing critical safety margins, removing necessary validation checks to save gas, or altering state access patterns in ways that could enable reentrancy or front-running. The goal of regression checks is to fail the CI/CD pipeline automatically if a proposed change violates predefined security invariants, creating a safety net for AI-suggested edits.

To implement these checks, you need a framework that can programmatically verify security properties. Tools like Foundry's forge with its invariant testing or Hardhat with plugins are ideal. You'll write invariant tests that assert conditions which must always hold true, such as totalSupply() never decreasing or user balances never exceeding the total supply. Run these tests against the optimized contract and compare results with the baseline version.

A practical approach is to maintain a dedicated test suite for security invariants separate from unit tests. For example, using Foundry, you could write:

solidityCopy
function invariant_totalSupplyConsistency() public {
    assertEq(token.totalSupply(), sumOfBalances());
}

Run this with forge test --match-contract SecurityInvariants --invariant. The test fuzzer will attempt to break the invariant by calling any contract function with random inputs. Any failure indicates a regression.

Integrate this check into your CI pipeline (e.g., GitHub Actions). The workflow should: 1) Check out the code with AI-suggested optimizations, 2) Compile the contracts, 3) Run the full security invariant test suite, and 4) Fail the workflow if any invariant is broken. This creates a gating mechanism; no optimized code can be merged unless it passes all security checks, ensuring automated optimizations don't compromise safety.

Beyond generic invariants, consider protocol-specific rules. For a lending protocol, you might assert that a user's health factor never drops below 1 without triggering liquidation. For a DEX, you could verify that pool reserves are always consistent after a swap. Document these critical invariants and treat their test files as essential project infrastructure. This layer of automated verification is what makes AI-assisted optimization viable for production DeFi applications.

You now have the blueprint for a system that can analyze transaction patterns, predict optimal gas parameters, and provide actionable suggestions. The core components are: a data ingestion layer using providers like Alchemy or QuickNode, a prediction engine leveraging models from libraries like scikit-learn or tensorflow, and a user-facing interface via a browser extension or wallet integration. The key is to start with a narrow, well-defined use case, such as optimizing simple token transfers on a single chain like Ethereum or Polygon, before expanding to complex DeFi interactions.

For development, begin by implementing the GasOracle class to fetch real-time data from a provider's RPC endpoint. Use the eth_feeHistory method to gather historical base fees and priority fees. Then, build a simple linear regression model that predicts the base fee for the next block based on recent network activity. You can test this model by simulating transactions using a forked mainnet environment with tools like Hardhat or Anvil. Remember to implement robust error handling for RPC calls and model inference failures.

Once your prediction model is functional, integrate it with a transaction simulation. Before a user signs a transaction, your framework should: 1) simulate it with the current gas settings, 2) run your model to get a suggested maxFeePerGas and maxPriorityFeePerGas, 3) simulate it again with the suggested settings, and 4) present the estimated savings and success probability. Use libraries like viem or ethers.js for simulation. Log all suggestions and outcomes to a database to create a feedback loop for retraining your model.

The final step is user trust and security. Clearly communicate that suggestions are estimates, not guarantees. Never modify a signed transaction payload; only suggest parameters before signing. Consider implementing a whitelist for trusted protocols to avoid suggesting optimizations for interactions with unaudited contracts. Open-source your methodology and submit for audits to build credibility. Resources like the EIP-1559 specification and Blocknative's Gas Platform documentation are excellent references for deepening your understanding of gas mechanics.