Zero1 Labs (𝟘,𝟷)

AI depends on data. Blockchains depend on trust. To combine them, privacy has to be solved. Cypher is Zero1 Labs’ FHE–EVM chain, designed for AI and LLM applications. It performs computations directly on encrypted data, ensuring inputs stay private while results remain correct. Built for confidential workloads, Cypher preserves scalability, enforces compliance standards, and enables developers to deploy decentralized AI applications without exposing sensitive information. Learn more: cypher.z1labs.ai

Zero1 Labs is fully open source on GitHub. Cypher, tooling, and supporting infrastructure, all public, all available to explore, run, and build on. → github.com/z1labs

Contextual GPT agent with Wallet Memory is on GitHub. A step toward agents that can recall and reason based on your wallet context. Open source, on GitHub: github.com/z1labs/mcp-cor…

Fully homomorphic encryption for privacy. Concurrent processing for speed. Composable configurations and modular design for flexibility. Built natively for AI apps from the ground up. Cypher is the execution layer that brings these together for on-chain AI.

Cypher Testnet, April 2026 85.1M transactions processed end-to-end under FHE. 13.2M DEAI transfers. 7.1M wallets, 120.1K encrypted TXs/day, 36.9M blocks, 262.1 GB encrypted data. Encrypted through execution.

DAO governance data, now structured for AI. The DAO Explainer is live on GitHub. A TypeScript + GraphQL server that connects to Tally and makes on-chain governance readable for agents and apps. github.com/z1labs/mcp-dao…

Cypher enables encrypted computation for AI, DePIN, and DeSci workflows. Process sensitive data without ever decrypting it. Inputs, models, and state stay private throughout. Purpose-built for AI data governance. cypher.z1labs.ai

MCP Core SDK v0.1 is live on GitHub. Build verifiable AI agents across chains and compute layers. Chain-agnostic. Pluggable transports. TypeScript-first. github.com/z1labs/mcp-cor…

Cypher Testnet: March 2026 84.3M transactions processed entirely under FHE. 6.9M addresses, 121.2K encrypted TXs/day, 36.6M blocks, 259.6 GB encrypted data. All staying encrypted through execution. Fully homomorphic encryption at EVM scale.

The full Zero1 Labs codebase is open-source. Our GitHub includes encrypted compute primitives, on-chain coordination tools, MCP integrations, and governance modules. Explore the repo → github.com/z1labs

Modern AI has a data ownership problem. Training datasets are often scraped without consent, hard to audit, and controlled by centralized platforms. Z1 is building infrastructure for decentralized AI with encrypted data pipelines and verifiable provenance.

AI can now compute on data it never sees. Cypher is the FHE-EVM chain, purpose-built for confidential AI. Encrypted inputs, verifiable outputs, full privacy by default. Deploy decentralized AI without ever exposing sensitive data.

Turn AI chats into onchain NFTs. Chat-NFT pins each session to IPFS, hashes context + model metadata via MCP, and mints an ERC-721 to your wallet. Explore → mcp.z1labs.ai

Cypher Testnet Milestone: February 2026 83.9M transactions on Cypher testnet, processed entirely under FHE. Data stays encrypted through execution. • 6.89M addresses • 122.1K encrypted TXs/day • 12.7M DEAI transfers • 35.6M blocks produced • 257.1 GB encrypted state processed • 193.8 GB storage used • 65,251 uploads in a single day Cypher is the EVM-ready network for encrypted computation, bringing private smart contract execution to real-world scale.

The MCP ZK Integrity Layer is live on Z1 GitHub. It adds zero-knowledge verification to the Model Context Protocol. Agents can generate proofs that: • a computation was executed correctly • context wasn’t altered • outputs match valid inputs All without revealing private data. Verifiable integrity, built directly into MCP execution. github.com/z1labs/mcp-zk-…

Built natively for AI apps. Engineered for encrypted execution. Cypher integrates FHE, concurrency, and composable architecture to support scalable, privacy-preserving computation on-chain. Execution remains encrypted at every stage.

Privacy is not about hiding data at rest. It’s about eliminating the moment of exposure. In most systems: encrypted → decrypted → computed → exposed. With Cypher: encrypted → computed → encrypted. Execution never transitions into plaintext. That changes what autonomous systems can safely do on-chain.

Fully Homomorphic Encryption (FHE) changes where privacy lives in a system. In most architectures today: 1. Data is encrypted while stored or transmitted. 2. It is decrypted before computation. 3. Execution happens in plaintext. That execution step is the exposure point. FHE removes that requirement. Computation is performed directly on encrypted data. The system never needs access to the raw inputs to produce a valid result. The output remains encrypted and can only be decrypted by the key holder. This makes confidential execution possible: - Smart contract logic without exposing inputs - AI inference without revealing model context or user data - Agents operating without leaking internal state Cypher is Z1’s implementation of this model, applying FHE at the execution layer so state does not need to be revealed for computation to occur.

MCP Context Badge Claim Module github.com/z1labs/mcp-nft… Implementation for minting ERC-1155 badges only when specific AI context conditions are met. Each claim is checked against the model used, a referenced context CID, and the claimant’s wallet. Proofs can come from an agent signature or an offchain verifier before minting proceeds. • Context + model checks (contextCID, modelHash) • Wallet-signed claims • Agent-signed or offchain proof support • ERC-1155 mint with context-linked metadata • Works via frontend or scripts Useful for cases where badges or credentials need to be tied to real interaction context.

Mintable AI Agents on Cypher, the first standard for turning trained models into verifiable digital assets. Each agent is an NFT bound to its SHA-256 model hash, with weights stored on IPFS or Arweave. Immutable. Verifiable. Optionally encrypted with FHE so your model stays private while proving it's real. Developers can monetize, license, and build an on-chain portfolio. Collectors can own functional AI with provable authenticity and performance history. → mcp.z1labs.ai

Most privacy systems protect data, not computation. Cypher uses FHE so execution happens on encrypted state. No point where data needs to be revealed.