Building a Simple Trading Agent with LLMs and Web3 Data

A minimal trading agent only needs three components. First you need a market data source such as a DEX subgraph or exchange API. Second you need a decision layer which can be as simple as a rules engine or as complex as a language model. Third you need a transaction sender that can interact with smart contracts.

Large language models are useful when the strategy depends on qualitative signals. For example you can embed news headlines, governance proposals and funding rate commentary into vectors and let the model reason about sentiment. The output is still a concrete decision such as increase exposure, reduce exposure or remain flat.

Implementation wise you should wrap the LLM behind a narrow interface. Give it only the data it needs and ask for a structured JSON response. A NestJS or Python service can validate that response, run additional safety checks and then call a contract via a library like viem or ethers.

This approach gives you an extensible agent that can evolve over time. You can swap out strategies without changing the surrounding infrastructure and you can add human in the loop review for high value trades. The result is a controllable system that uses AI where it adds real value rather than everywhere.