The past troubles of cross-chain data stemmed from scattered raw logs, inconsistent standards, and expensive computations, ultimately relying on centralized indexing services for support. Lagrange's approach is to engineer the entire chain of 'index → computation → proof → verification': first, using provable methods to map multi-chain history into a ZK-friendly database, then executing complex SQL-level queries on it, and finally returning the proof for verification by smart contracts. The direct result is that the contract side can obtain verifiable query conclusions, rather than 'trusting a certain API'. The official product page clearly defines 1.0 as 'SQL-based ZK Coprocessor', which is key to understanding its differences from the traditional 'pull data and then prove' paradigm.
This method is not just theoretical design. The public test network of Euclid starting in 2024 focuses on the idea of a 'verifiable database': first, let developers build provable tables from cross-chain data in the public test environment, and then run queries and recursive proofs on top of them. Early technical blog posts emphasized its differences from 'typical ZK coprocessing': not every time starting with 'digging a small piece of data from the chain and then computing', but first making the on-chain state into a verifiable index, and then performing parallel computations on the index. For complex statistics across contracts and multiple chains, this can significantly reduce costs and delays.
The landing effects can be quantified. The first phase review of Euclid disclosed several hard metrics: integration support for L2s such as Base / Fraxtal / Mantle, running over 81K+ queries, 60K+ unique users, and 610K+ proofs during the testing phase, and provided typical performance metrics—initiating queries on 1 million storage slots on L2 and completing the full-link proof/verification in approximately 1 minute; simultaneously achieving about a 2-fold improvement in preprocessing speed compared to the initial test. These numbers correspond to the system performance after the simultaneous effect of 'database-like + high parallelism + recursion'.
Performance comes from specific engineering choices. The team clearly chose Plonky2 as the proof system, aligning with the parallel strategy of 'breaking large computations into many recursive small circuits'; at the same time, they adopted a method of 'only computing changes (delta)' on the self-developed Reckle Trees path to avoid recalculating the entire amount each time. For developers, this means that most performance details are handed over to the underlying stack, with the upper layer focusing on SQL/interfaces, reducing reliance costs on cryptographic details.
In order to prove that 'it runs smoothly and can be returned', networked productivity is also needed. In 2024, Lagrange deployed the ZK proof network on top of EigenLayer as an AVS (Actively Validated Service): a distributed prover cluster run by multiple institutional nodes is responsible for 'picking up tasks - generating proofs in parallel - returning for on-chain verification'. The official list of initial and subsequent operators includes infrastructure teams such as Coinbase, OKX, and Staked (Kraken), and the operational scale will subsequently expand to 85+ levels. For users of the Coprocessor, this upgrades the proof capacity from a 'single-machine bottleneck' to a 'network supply'.
The data layer is not just for 'looking good'. The goal of the ZK Coprocessor is to make scenarios that require evidence, such as cross-chain fund tracking, decentralized reputation, governance and airdrop settlements, and historical behavior metrics, naturally available at the contract layer. When you write 'multi-chain balance/interactions' as a SQL query within a DApp and can obtain a proven result in a single transaction, both user experience and trust boundaries are established. Engineering updates and metrics panels publicly announce monthly changes in 'proof speed, concurrent throughput, and update efficiency', which helps the ecosystem view it as infrastructure rather than a 'research project'.
Finally, there is product stability. The 1.0 page places 'initiating custom SQL proofs directly from the contract' and 'case studies with partners' on the first screen, indicating that it is already aimed at producers and not just limited to public testing. Combined with data on chain coverage, end-to-end latency, and user volume from the Euclid review, it can be confirmed to be in a usable state. The next steps worth monitoring include index coverage expansion, limits on complex JOINs, more L2/L3 access, and combinations with cross-chain messaging/bridge systems.
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