OpenRank primary sources — 2026-05-10

What I reviewed

• Official site and docs:
  • https://openrank.com/
  • https://docs.openrank.com/
  • https://docs.openrank.com/the-reputation-stack/openrank-protocol.md
  • https://docs.openrank.com/reputation-algorithms/eigentrust
  • https://docs.openrank.com/openrank-sdk/introduction
• Official repositories:
  • https://github.com/openrankprotocol
  • https://github.com/openrankprotocol/openrank
  • https://raw.githubusercontent.com/openrankprotocol/openrank/main/README.md
  • https://github.com/Karma3Labs/openrank-sdk
  • https://raw.githubusercontent.com/Karma3Labs/openrank-sdk/main/README.md

Key takeaways from primary sources

• OpenRank’s core reusable mechanism is not a fixed reputation score but a pipeline: define a trust graph, choose compute parameters, run a graph algorithm, and publish verifiable outputs that downstream apps can consume.
• The protocol docs make clear that publication and verification are part of the product surface. Input graphs and output rankings are meant to be available on a DA layer, not hidden inside one company’s API.
• The most important governance surface is upstream of the algorithm: who constructs the graph, which local-trust edges count, which peers receive seed trust, and how much seed confidence is injected.
• OpenRank’s EigenTrust docs are especially useful because they reject context-free reputation. The system expects developers to encode a use-case-specific notion of pairwise trust rather than treat all follows, transfers, votes, or attestations as interchangeable.
• The SDK docs show OpenRank as middleware for developers. It is designed to sit between raw social/onchain data and end-user applications that need ranking, recommendation, search, or sybil resistance.
• The listed live use cases show the protocol’s ambition to generalize across social graphs, transaction graphs, NFT activity, governance signals, and software-security attestations.

Specific source signals worth retaining

• Protocol docs: strongest concise statement of what OpenRank guarantees — open job submission, DA-backed input/output availability, and verifiable results.
• EigenTrust docs: best explanation of local trust, seed trust, seed confidence, sybil resistance, and why context-specific graph construction matters.
• SDK docs / README: useful for seeing where operator discretion sits in practice, especially around custom datasets, graph heuristics, and publishing results.
• Repo README: helpful architectural summary of block builder, computer, verifier, DA interface, and SDK components.
• Landing page: good shorthand for the product thesis — decentralized ranking and reputation infrastructure for the open web.

Why this entry belongs in the corpus

• It strengthens the identity / reputation / contributor-legibility side of the corpus with a protocol that sits one layer downstream from attestations and social graphs.
• It gives a clean comparison class for EFP, Human Passport, SourceCred, EAS, and Sign Protocol by isolating the ranking-compute layer from the signal-generation layer.
• The reusable insight is that many systems that look different at the UI layer reduce to the same control problem: who creates the trust graph, who gets seeded, which algorithm runs, and how widely the results can be reused.