How The Graph Keeps AI Applications Fueled With Onchain Data
For most of its history, web data was consumed by humans. Search engines indexed it, but the information was read by real users. As the agentic web takes center stage, the primary consumers of this data are changing. The onus now increasingly falls to autonomous AI agents to make sense of information to perform tasks ranging from trading assets to booking services and more, without human intervention.
For these agents, data is not just information; it's fuel. And like a high-performance engine, an AI agent is only as reliable as the fuel it consumes. If an agent is fed delayed or incorrect data, it executes that mistake at machine speed, potentially cascading across protocols.
As a result, the maxim that "you are what you eat" applies to agents as it does to humans. To thrive, AI agents need a diet of real-time, high-quality onchain data. This is where The Graph's data infrastructure, specifically and , becomes the critical supply chain for the AI economy.
In traditional centralized systems, one trusts the database administrator. In web3, users trust the blockchain. That said, blockchain data in its raw form isn't easily absorbed. It needs to be processed before it is useful, and that's where Indexers on come in.
An AI agent can't efficiently scan Ethereum's entire history to find a specific historical token price or governance vote. It needs an Indexer to organize that data and deliver it in a timely fashion.
A centralized API could be used for this, but the problems are obvious: it introduces a single point of failure. If the API goes down or is manipulated, the agent acts on a false reality. What makes The Graph data products well-suited for AI applications is that they produce deterministic outputs. Deterministic data means that any Indexer running the same Subgraph or Substreams module against the same blockchain inputs will produce the same result. That consistency is what makes the data trustworthy enough for agents to act on at scale.
And when there's real money at stake, as there invariably is with agents trading and rebalancing DeFi portfolios, a lot is riding on that data being correct. This is where The Graph comes into play with Subgraphs and Substreams.
The Graph provides two distinct data products, each serving different needs within an AI application stack. The first of these is Subgraphs, which, to extend the culinary metaphor, are like a well-stocked pantry. Each Subgraph forms an open API that organizes blockchain data into a specific schema, queryable via .
Subgraphs produce deterministic data. Because the indexing logic is open source, any party can inspect how the data was extracted and structured from raw chain activity. On , Indexers stake tokens to guarantee the accuracy of their work, creating economic incentives for reliable performance.
If an agent needs to check the current owner of a specific NFT before executing a trade, or the price of ETH at a given block, a Subgraph provides that state instantly. Whatever digital dish the agent desires to dine on, if it's stocked in the Subgraph larder, it'll be served up readily.
Completing this data stack is Substreams. A parallelized, streaming-first solution, Substreams is designed for massive throughput. Developers write Rust modules that process blockchain data in parallel, transforming it into any format needed at extremely high speeds.
Substreams is particularly well-suited to powering the data pipelines that AI applications and large language models depend on for training and fine-tuning. Rather than querying a live endpoint, AI and analytics teams use Substreams to ingest and transform terabytes of blockchain history, producing clean, structured datasets orders of magnitude faster than linear indexing.
Say an AI team wants to train a model to predict DeFi liquidity flows. Substreams can absorb the entire history of transactions, transforming raw block data into a training-ready dataset in hours. That's a fundamentally different use case from what a Subgraph serves, and the distinction matters when designing an AI application stack.
Push the plate aside and consider a working example of agentic data delivery in action:
is an AI launchpad that allows users to create and tokenize AI agents, each with its own agent keys that are traded on bonding curves. This requires real-time pricing and ownership data. Traditional RPC providers were too slow and costly to handle the complex, real-time data streams generated by thousands of agent launches and trades.
The solution came via Subgraphs. Following integration, CreatorBid achieved sub-second data freshness, ensuring agents and users see price changes the moment they happen. This eliminated the need to maintain custom indexers, allowing the team to focus on agent logic rather than data plumbing.
And because the data is indexed through The Graph Network, the economic activity of these AI agents is transparent and consistently reproducible by anyone in the CreatorBid ecosystem. When the data problem is solved, the greatest impediment to agents realizing their full potential is removed.
The early internet organized information discovery through centralized search engines. The agentic internet requires something more demanding: infrastructure that allows machines to retrieve and act on high-quality, consistently produced data autonomously.
Subgraphs and Substreams serve that need in different ways. Subgraphs give agents fast, queryable access to structured onchain state. Substreams give AI teams the throughput to build and train on rich historical datasets. Neither replaces the other; they address different parts of the same problem.
The Graph's data products don't make AI agents intelligent. Instead, they ensure that when intelligence is applied, it operates on structured, deterministically produced data grounded in reliable onchain sources. That's crucial because the integrity of the input determines the integrity of the outcome.
As autonomous systems continue to expand across finance and digital commerce, the infrastructure that feeds them will become as strategically important as the models themselves. Within that stack, deterministic data indexing and high-performance streaming pipelines are the fuel lines that feed the agent economy.
About The Graph
The Graph is a suite of blockchain data infrastructure products that extract, process, and deliver scalable blockchain data solutions across 60+ networks. The Graph enables application developers, data analysts, AI agents, and enterprise teams that need structured, real-time access to blockchain data. Products include Subgraphs, Firehose, Substreams, and Amp. As of early 2026, The Graph has served over 1.27 trillion queries to more than 75,000 projects, powered by a network of independent Indexers around the world.
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