DataHaven Decentralized Storage Network
AI-first decentralized storage secured by EigenLayer, offering verifiable storage for training data, models, and Web3 applications.
- Source repo
- datahaven-xyz/datahaven
- Stars
- ★ 7.9k
- Last updated
- 6mo ago
- License
- GPL-3.0
- Primary language
- Rust
- FA score
- 32/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free, no paid service needed
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- AI/ML teams: store training datasets, model weights, and agent configurations with cryptographic integrity proofs, enabling federated learning and verifiable AI pipelines.
- Not a fit if
- Teams needing fully offline or single-machine local operation; it depends on EigenLayer and Ethereum
- Beginners wanting a turnkey setup without Docker, Kurtosis, or Kubernetes
- Teams needing proven production maturity rather than a testnet-stage project
- Source review
- 32/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
DataHaven is a Substrate-based decentralized storage and retrieval network designed for applications requiring verifiable, production-scale data storage. It leverages StorageHub's architecture to separate storage from verification: data is stored off-chain while cryptographic commitments (Merkle roots) are anchored on-chain. Security is provided by EigenLayer's restaking protocol, with validators registered as operators and subject to slashing. The network employs a two-tier storage provider model: Main Storage Providers (MSPs) are user-selected and serve data, while Backup Storage Providers (BSPs) ensure redundancy through replication and face proof challenges that result in on-chain slashing if failed. DataHaven is EVM-compatible, supporting Solidity smart contracts and standard Ethereum tooling, and includes a trustless cross-chain bridge via Snowbridge.
DataHaven provides a full decentralized storage stack: users select an MSP, create a bucket, and upload files; files are chunked (8KB), hashed into Merkle trees, and the root is committed on-chain. BSPs replicate data based on replication policies and are periodically challenged to prove data custody; failures lead to slashing. A Fisherman service audits proofs and triggers challenges, while an Indexer indexes on-chain events for queries. The contracts/ directory contains EigenLayer AVS smart contracts (DataHavenServiceManager, RewardsRegistry, etc.), and operator/ contains the Substrate node with custom pallets (external validators, rewards, transfers) and Frontier for EVM compatibility. The test/ framework uses Kurtosis to deploy a full local environment including Ethereum nodes, the DataHaven node, storage providers, and Snowbridge relayers.
- AI/ML teams: store training datasets, model weights, and agent configurations with cryptographic integrity proofs, enabling federated learning and verifiable AI pipelines.
- DePIN projects: maintain persistent storage for IoT sensor data, device configs, and operational logs with provable data lineage.
- Real World Asset platforms: immutably store asset documentation, ownership records, and compliance data with on-chain verification.
- DApp developers: leverage EVM compatibility to deploy Solidity smart contracts that utilize DataHaven's storage capabilities.
- Organizations needing cross-chain interoperability: transfer tokens and messages between Ethereum and DataHaven via Snowbridge.
How do you install or deploy this agent?
Install dependencies: Kurtosis, Bun (v1.3.2+), Docker, Foundry, and Rust for building the operator. Optional: Helm for Kubernetes deployments. Clone the repository and install test dependencies: cd test && bun i.
How do you use this agent?
Launch a local network: cd test && bun cli launch (interactive prompts). This deploys a full local environment via Kurtosis. Run E2E tests: bun test:e2e or bun test:e2e:parallel. Develop smart contracts: cd contracts && forge build && forge test. Build the operator: cargo build --release --features fast-runtime && cargo test. Regenerate bindings after contract/runtime changes: bun generate:wagmi and bun generate:types. Build Docker image: cd test && bun build:docker:operator.
What are this agent's strengths and limitations?
- Verifiable storage with Merkle proofs and on-chain commitments eliminates the need to trust intermediaries.
- Economic security via EigenLayer restaking, with slashing for validator misbehavior.
- EVM compatibility supports Solidity contracts and existing Ethereum tooling.
- Two-tier provider model (MSP/BSP) balances performance and redundancy.
- Platform-specific: relies on EigenLayer, StorageHub, and Substrate; migration to other ecosystems requires substantial adaptation.
- High deployment complexity: requires Kurtosis, Docker, and Rust toolchain; not beginner-friendly.
- Potential costs: on-chain commitments and proof challenges incur gas fees; storage provider services involve economic incentives that may be passed to users.
- Early-stage project with testnet focus; production maturity is unproven.
How does this agent compare with similar options?
Compared to other decentralized storage solutions like Filecoin (independent network with proof-of-spacetime) and Arweave (permanent storage), DataHaven offers shared security via EigenLayer and EVM compatibility. However, these comparisons are not explicitly mentioned in the source material and should be considered cautiously.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| DataHaven Decentralized Storage Network This agent | 32 · Major gaps | CLIFree | ★ 7.9k | 6mo ago | Rust | — |
| Ripple Community Wallet | 34 · Major gaps | Web appFree | ★ 2.3k | 3mo ago | TypeScript | — |
| MCP Memory Service | 68 · Some gaps | MCP serverFree | ★ 2k | today | Python | ChatGPT · Codex · Claude Code · Claude.ai |
| CloudBase AI Toolkit | 59 · Major gaps | Agent plugin / skillFreemium | ★ 1.1k | today | TypeScript | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows a blockchain infrastructure project, but no explicit information on least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust criteria are unmet, hence score 0.
Self-consistency: README, architecture diagram, and CI config are broadly consistent, but code implementation not deeply verified, score 2. Dependency availability: Lists many external dependencies (e.g., Kurtosis, Bun, Docker) but no version pinning or integrity checks, score 1. Failure messages: Error handling in CI and test scripts, but lacks clear user-facing failure prompts, score 1.
Audience and scenarios: README clearly identifies use cases like AI, DePIN, RWA, score 2. Capability boundaries: Describes core features but no explicit limits or boundaries, score 1. Trigger precision: CI triggers are clear, but product-level trigger mechanisms not specified, score 1. Environment fit: Provides multiple deployment environments (local, stagenet, testnet, mainnet) and Docker support, score 2.
Information architecture: Clear directory structure with READMEs for each module, score 2. Install notes: Provides quick start and dependency installation guide, score 2. Naming stability: No API or naming stability commitments, score 1. Examples and FAQ: Has usage examples but no FAQ, score 1. Known limitations: No known limitations mentioned, score 0. License: GPL-3.0 license file complete, score 2. Versioning and changelog: CI has release automation but no changelog provided, score 1. Maintenance responsibility: No clear maintainer or responsible party, score 1.
Output usability: Provides CLI and test commands but no output format or integration details, score 1. Marginal value: As a decentralized storage network, has unique value, score 2. Cost-benefit: No performance or cost data provided, score 1.
Claim traceability: Claims in README not linked to specific code or tests, score 1. Cross-source corroboration: Relies solely on single repository, no external verification, score 1. Fact-inference separation: Facts and inferences mixed in documentation, not clearly separated, score 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- No security audit or vulnerability report provided, dependency security unknown.
- No user confirmation mechanism specified, may automatically execute high-risk operations.
- No data flow transparency explanation, users cannot track data movement.
- No rollback mechanism provided, failures may be unrecoverable.
- No known limitations listed, potential issues may be hidden.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
RT1400 Runtime RT1400
Release notes excerpt
DataHaven RT1400 runtime release - Stagenet & Testnet environments only This runtime release brings incremental improvements in the validator rewards & slashing core mechanics. Runtimes Stagenet ``` ✨ spec_version : 1400 🏋 size : 1917848 ️⃣ sha256 : 0x1a324022069b7499b8d0a080173de8de11a0632aec8d87dd1b8470b0f73417fd ️⃣ blake2-256 : 0xb8914f35f930d291acdec60740bc5435d1f76d17ba570f0919960517174bd7ca 🗳️ proposal (authorizeUpgrade) : 0xb251415411e58200420d6a23337f29aef9f8b4d0f14623012dda992f8d67ec78 ``` …View on GitHub ↗ -
v0.26.0 DataHaven v0.26.0
Release notes excerpt
DataHaven v0.26.0 release - Stagenet & Testnet environments only The main highlight of this client release is the upgrade to the [StorageHub v0.4.3 release](https://github.com/Moonsong-Labs/storage-hub/releases/tag/v0.4.3). DataHaven Changes * fix: :bug: wire trusted_msps only when the provider is a BSP by @TDemeco in https://github.com/datahaven-xyz/datahaven/pull/458 * build: ⬆️ upgrade to StorageHub v0.4.3 by @TDemeco in https://github.com/datahaven-xyz/datahaven/pull/468 StorageHub Changes …View on GitHub ↗ -
RT1300 Runtime RT1300
Release notes excerpt
DataHaven RT1300 runtime release - Stagenet & Testnet environments only This runtime release brings incremental improvements for the integration with EigenLayer, specifically regarding the validator set update flow. Runtimes Stagenet ``` ✨ spec_version : 1300 🏋 size : 1915005 ️⃣ sha256 : 0xde0ee076c0a0e899f946749d771cb22a72801dc284b95391f7128155876885e6 ️⃣ blake2-256 : 0x5d2b0482495cfd3e6d6863937725871d20795d66fc82f96b2cd915a72f362e5d …View on GitHub ↗