Chainlink integrates U.S. Commerce Department macroeconomic data
Chainlink and Pyth to deliver official U.S. GDP metrics across nine blockchains
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Chainlink has launched a new set of data feeds that deliver official U.S. Department of Commerce macroeconomic statistics directly to blockchains, the company announced on Thursday.
The initiative is part of a broader collaboration that also involves Pyth Network, with both oracle providers confirming they are working with the Commerce Department to bring Bureau of Economic Analysis (BEA) data on-chain.
The program makes government-released indicators such as gross domestic product (GDP), the Personal Consumption Expenditures (PCE) Price Index, and Real Final Sales to Private Domestic Purchasers available on-chain through decentralized oracle networks.
Initial deployment spans ten blockchains, including Ethereum, Base, Avalanche, Arbitrum, Optimism, Mantle, Linea, Botanix, Sonic and ZKsync.
The integration builds on Chainlink’s broader role as a provider of verifiable data feeds, which already support functions ranging from token price updates to weather insurance claims. By extending this framework to US government economic data, Chainlink and Pyth position themselves as bridges between public institutions and blockchain ecosystems.
The feeds are secured by the same decentralized infrastructure that underpins Chainlink’s price oracles and Pyth’s aggregated data network.
Commerce Secretary Howard Lutnick said earlier this week that the department would begin publishing GDP and other statistics on-chain, signaling a potential expansion of the model to additional U.S. agencies.
Primary documentation of the integration, including live contract addresses for the data feeds, is already available on Chainlink’s developer portal. The announcement follows prior expansions into weather and sports data, continuing Chainlink’s strategy of embedding external information directly into blockchain systems.
Updated 8/28/2025 at 10:22 a.m. ET to include Pyth involvement.
This article was generated with the assistance of AI and reviewed by editor Jeffrey Albus before publication.
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