Arweave

Type: Infrastructure - Decentralized Data Storage Website: https://arweave.org

Overview

Arweave is a decentralized, permanent storage network optimized for immutable data archiving and high-throughput access. AR.IO's Turbo bundling solution efficiently handles billions of requests monthly, making it ideal for managing Amadeus AI agent datasets, model checkpoints, and provenance records at scale.

By integrating Arweave, Amadeus ensures persistent, verifiable storage for AI compute outputs without burdening the main chain — enabling a fast, secure, and decentralized layer for long-term agent memory and auditability.

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Planned Capabilities for Amadeus

• Offload AI data (weights, logs, metadata) to permanent, verifiable Arweave storage.

• Use Turbo bundling via the TypeScript SDK or AR.IO API for high-throughput uploads and retrieval.

• Store lightweight on-chain hashes (e.g., SHA-256) for provenance without embedding full data in L1.

• Integrate Rust Arweave crates for native signing and uploads, ensuring authenticity and future Rust-Turbo compatibility.

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Example Use Cases

AI Agent Data Persistence: Store and reference immutable model states, logs, and memory snapshots tied to agent IDs.

Compliance & Provenance: Enable audit trails for AI training datasets or model lineage through hashed metadata stored permanently.

Decentralized Knowledge Graphs: Archive structured outputs and public knowledge embeddings contributed by agent swarms.

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Current Status

Integration planned using Rust-based Arweave crates for core signing and upload flows, combined with AR.IO's Turbo TypeScript SDK for high-throughput data bundling.

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Upcoming Deliverables (Planned)

Q1 2026: Implement hybrid integration—Rust crates for authenticated data uploads and Turbo SDK/API for high-throughput storage. Deploy a lightweight on-chain registry contract referencing stored data hashes for provenance and accounting.

Pontential extensions:

Extend support for multi-token payments (potentially including $AMA) and optimize Turbo bundling for agent-generated datasets and model checkpoints at scale.