OpenRank and EigenCloud turn reputation and ranking from a black box into verifiable public infrastructure. Anyone can audit reputation scores, verify algorithms, and challenge results — backed by cryptoeconomic guarantees that enforce honesty.

Reputation and Trust are Cornerstones of the Internet

Every day we delegate trust to many different parties on the internet. Search results, marketplace listings, social feeds, airdrop lists - each of these is an output of a proprietary system that decides what you see, who gets paid. Because of this, a handful of problems show up again and again:

No agency. You don’t have control over your feeds or search results, algorithms do.

No explanation. You rarely know why something was ranked ahead of something else, or what signals were counted.

No recourse. If a ranking or result looks wrong, there’s no way to audit it, challenge it, or hold anyone accountable.

No portability. Your identity and reputation are trapped in silos; rules can change overnight and years of context evaporate.

This isn’t just social media. In search, you trust unseen weighting; in marketplaces, you trust star systems and review filters; in crypto, you trust opaque heuristics for airdrops, retro funding, and allowlists. In each case, trust is outsourced to a centralized ranking operator — without a receipt that shows what was weighted, when it was updated, where the data came from, or how the decision was computed.

And the problem is accelerating. Fraud, spam, manipulation, and misaligned incentives are everywhere. The cost of outsourcing trust to centralized intermediaries keeps climbing, while the cost of producing noise has collapsed: spam is effectively free, and AI can fabricate convincing signals at scale. At the same time, the stakes of “who gets distribution or money” grow higher every day.

Solving these problems requires more than open data — it needs verifiable compute infrastructure. OpenRank provides the reputation protocol, while EigenCloud powers the verifiable compute layer — ensuring rankings are transparent, challengeable, and economically enforced.

OpenRank is a reputation protocol built for this shift. It turns ranking and reputation algorithms from a private product decision into shared infrastructure. Developers compute reputation scores over open data, and anyone can verify cryptographically that the computation obeyed the declared rules. Open-source code is table stakes, but it doesn’t guarantee whether the code that ran is the code you read. “Verifiable” adds the missing property — each run of an algorithm produces commitments (cryptographic fingerprints) and an onchain trail that anyone can challenge. If a recomputation shows the result was wrong, the operator can be economically penalized. Attaching consequences to dishonesty elevates reputation from dashboards to infrastructure.

In practice, this enables social feeds you can explain, leaderboards you can audit, and incentives you can justify. OpenRank and EigenCloud unlock a marketplace of algorithms that organize the open internet — social feeds, trustworthy agent networks, crypto tokens, and more — on open rails.

Powering reputation and ranking in crypto and social

OpenRank is emerging as a leading reputation protocol for crypto developers. It already powers feeds on open social graphs like Farcaster, drives token recommendations in wallets, and enables targeted airdrops to reputable users. Over 500 token airdrops have leveraged OpenRank, distributing millions in rewards. We see growing demand for verifiable ranking systems that solve real problems:

• Airdrops & Public Goods Funding. You can’t reward “real contributors” without Sybil resistance and auditability. OpenRank, using EigenCloud’s verifiable compute, builds verifiable leaderboards over open datasets — anyone can see how ranks were computed, challenge them, or fork the system permissionlessly.
• Social Discovery & Curation. Social feeds shape culture and value. With open social graphs and transparent ranking, apps can finally show why a post ranks high — powering more curated, personalized consumer experiences.
• Token Discovery & Recommendation. The token space is noisy. OpenRank computes trust & safety scores across onchain + social data, helping users discover credible tokens and avoid rug pulls.
• Agent & API Reputation. As bots and agents transact autonomously, they need verifiable, updatable reputations. OpenRank enables ranking of agents just like users or tokens, unlocking reputation-gated access and payments.

How OpenRank achieves cryptoeconomic honesty with EigenCloud

OpenRank protocol operates through a network of compute nodes that execute reputation algorithms (like Eigentrust, Hubs and Authorities) on open data sets, while verifier nodes independently validate the results. All computations are backed by cryptographic proofs and secured through EigenLayer's restaking mechanism, creating economic incentives for honest behavior and penalties for malicious actors. By combining trusted execution environments (TEEs), decentralized data availability, and cryptoeconomic security, OpenRank enables a new paradigm where algorithmic transparency and verifiability become the foundation for trust and reputation in crypto or any open social networks.

OpenRank couples a verifiable compute pipeline with EigenCloud security:

• Architecture - Inputs (“seed/trust” data) and outputs (scores) are stored on a data‑availability layer; developers interact through the OpenRank SDK; a Smart Contract Manager coordinates jobs. Compute runs in two TEE enclaves—a Computer that executes, and a Verifier that independently checks—while EigenLayer supplies restaked collateral for slashing.
• Lifecycle - Jobs follow a clear path: submit → compute & commit → (optional) challenge. Nodes download inputs, execute the algorithm, produce proofs, upload results, and post a commitment on‑chain. Verifiers can recompute in whole or in part; if a mismatch is proven, the operator’s stake can be slashed.

The effect is simple to reason about: reputation scores aren’t just “our best effort”—they’re provably derived from declared inputs and algorithms, with skin‑in‑the‑game for correctness.

Steps for each reputation compute request

1. Job Submission

• Upload input dataset to the data availability (DA) layer.
• Create a Job Description that references the input (URIs + hashes), the algorithm ID/version, and its hyper-parameters.
• Submit an onchain request (to the Manager contract) that points to the Job Description. The contract emits ComputeRequested(jobId).
• Registered compute nodes watch for this event and pick up eligible jobs.

User                 DA Layer            Smart Contract     Compute Node
 │                     │                       │                 │
 │ Upload job desc     │                       │                 │
 ├────────────────────►│                       │                 │
 │                     │                       │                 │
 │ submitMetaComputeRequest(jobId)             │                 │
 ├────────────────────────────────────────────►│                 │
 │                     │                       │                 │
 │                     │                       │ Emit Event      │
 │                     │                       ├────────────────►│
 │                     │                       │                 │
 │                     │                       │◄────────────────┤
 │                     │                       │   Monitor       │

2. Computation Phase

• The selected compute node downloads the inputs and Job Description from the DA layer and verifies hashes.
• It executes the algorithm, producing scores/outputs and metadata, and derives a commitment (merkle root) over the results.
• The node uploads the results bundle to the DA layer and posts an onchain result.
• This opens a challenge window during which anyone can dispute the result.

Compute Node         DA Layer             Smart Contract
   │                   │                       │
   │ Download data     │                       │
   ├──────────────────►│                       │
   │                   │                       │
   │ Execute algorithm │                       │
   ├───────────────────│ (internal)            │
   │                   │                       │
   │ Generate proofs   │                       │
   ├───────────────────│ (internal)            │
   │                   │                       │
   │ Upload results    │                       │
   ├──────────────────►│                       │
   │                   │                       │
   │ submitMetaComputeResult(commitment)       │
   ├──────────────────────────────────────────►│

3. Verifier Phase

• Verifier nodes (or challengers) fetch the inputs, Job Description, and results, then re-execute fully or by sampling to check correctness.
• If outputs match the commitment, verifiers post an attestation; after the challenge window, the job is finalized.
• If a mismatch is proven, a challenge is posted; the EigenCloud/EigenLayer process re-executes, and a dishonest operator can be slashed. The job is then re-assigned or corrected and finalized with an updated commitment.
• The finalized result remains addressable via the on-chain reference and the DA resultsId.

Verifier Node       DA Layer             Smart Contract       EigenLayer
    │                  │                       │                  │
    │ Download results │                       │                  │
    ├─────────────────►│                       │                  │
    │                  │                       │                  │
    │ Verify comp.     │                       │                  │
    ├──────────────────│ (internal)            │                  │
    │                  │                       │                  │
    │ (if incorrect)   │                       │                  │
    │ submitMetaChallenge(computeId)           │                  │
    ├─────────────────────────────────────────►│                  │
    │                  │                       │                  │
    │                  │                       │ Slash operator   │
    │                  │                       ├─────────────────►│

What developers get with OpenRank

• A protocol, not an opaque product/service. Submit jobs via SDK, select ranking algorithms, and receive results with on-chain commitments you (and your users) can verify.
• Portable, explainable reputation. Scores that can be inspected, re‑run, and embedded across clients.
• Economic guarantees. Re-staked operators with slashing align incentives for honesty, not opacity.

Cura - a social app for communities built on OpenRank and EigenCloud

Cura, a Reddit-like, crypto‑native social app illustrates what becomes possible. It addresses the core needs of communities that want to set their own rules, curate their content and reward genuine contributions. By leveraging OpenRank, Cura equips each community with custom feeds and algorithms, dynamic leaderboards, and integrated community currencies and wallets.

OpenRank computes the scores that power a verifiable feed and a weekly contributor leaderboard; EigenCloud ensures each run is provably correct, challengeable, and economically enforced. Distributing weekly rewards stops being a spreadsheet exercise and becomes a public, auditable ceremony.

Cura is just one instance; the pattern generalizes anywhere distribution or money follows an algorithm. As billions of users transact across thousands of chains, trust can’t be a private affordance; it has to be a public good. That means algorithms you can read, results you can verify, and operators with skin in the game. OpenRank provides the reputation layer; EigenCloud turns honesty into an economic inevitability. Apps like Cura show how those guarantees feel to end‑users.

EigenCloud and OpenRank are partnering with developers and communities across crypto, social, and financial ecosystems to make verifiable reputation a shared standard. If reputatuon and ranking drives distribution or rewards in your product, we’d love to collaborate.

Check out our docs

https://docs.eigencloud.xyz/ - EigenCloud Documentation

https://docs.openrank.com/ - OpenRank Documentation

https://github.com/openrankprotocol/openrank-tee - Main OpenRank protocol GitHub repository