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zkonduit/ezkl

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ezkl is an engine for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). Use it from Python, Javascript, or the command line. observed · 2026-10-05

github.com/zkonduit/ezkl · homepage · Rust observed · 2026-10-05

Health v2 · maintenance only

69/100

  • Activity 60
  • Release rhythm 64
  • Longevity 100

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 6
  • age_days: 1568
  • days_rel: 242
  • days_push: 242
  • n_releases_24m: 32

Full methodology

Adoption not part of the score

1227 stars · 212 forks observed · 2026-10-05

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EZKL is a Rust-based library and command-line tool that converts deep learning models and arbitrary computational graphs (exported as ONNX) into ZK-SNARK circuits, enabling provable machine-learning inference using the Halo2 proof system. Generated proofs can be verified cheaply on-chain (EVM), in a browser, or on a device, and the tool is usable from Python, JavaScript, or the command line.

Use cases

  • prove a neural network was run on private data without revealing the data
  • prove a private model was run correctly on public data without revealing model weights
  • verify ML inference results on-chain in an Ethereum smart contract
  • generate zk-snark proofs from ONNX models
  • audit that a reported model accuracy benchmark is genuine
  • run verifiable AI/analytics where results must be trusted
  • do zero-knowledge machine learning inference (ZKML)

When to choose

  • You need verifiable AI: provable correctness of model execution where inputs or weights must stay private
  • You want a smart contract to verify ML inference output without running the model on-chain
  • Your models come from PyTorch/TensorFlow and can be exported to ONNX
  • You need proofs verifiable on EVM, in browsers, or on resource-constrained devices

When to avoid

  • You only need fast or cheap ML inference with no trust/verification requirement - ZK proving adds significant overhead
  • Your computational graph relies on operations the ZK circuit backend cannot yet support
  • You require a project with an explicit open-source license for procurement or compliance purposes

Facets

library · maturity active

machine-learning deep-learning cryptography cli artificial-intelligence machine-learning deep-learning privacy blockchain security cross-platform cli python rust wasm browser zkml zero-knowledge zk-snarks onnx halo2 verifiable-computation on-chain-verification evm proof-system python-bindings javascript-bindings verifiable-ai

2 sources

Member repositories

RepositoryRoleHealth v2
zkonduit/ezklmain69

For agents

markdown · JSON · MCP: product_card(name="zkonduit/ezkl")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem