How does Web3 data infrastructure work? Analysis of Unibase decentralized data network architecture.

AI agents are evolving from single conversation tools into autonomous digital entities that can execute tasks across platforms. This evolution imposes new requirements on infrastructure: AI needs long-term memory, cross-platform collaboration, and verifiable data sources. However, traditional AI systems rely on centralized databases and limited context windows, causing agents to lose state after each interaction and preventing them from accumulating experience.

Unibase attempts to answer one question: how to build a decentralized data infrastructure for AI agents, enabling them to remember, collaborate, and evolve like long-lived digital entities?

The project positions itself as a high-performance decentralized AI memory layer, specifically designed to provide long-term memory and cross-platform interoperability for autonomous AI agents. Its core goal is not to enhance a single model's inference ability, but to build an infrastructure where AI agents can exist long-term and operate collaboratively. This article will conduct a systematic technical breakdown around four dimensions of Unibase: data collection and storage mechanisms, decentralized indexing system, AI data invocation logic, and data trustworthiness verification mechanisms.

Three-Layer Architecture of Web3 Data Infrastructure

Understanding Unibase's data network operation first requires grasping its overall architecture. Unibase consists of three tightly integrated core modules: Membase (decentralized memory layer), AIP Protocol (agent interoperability protocol), and Unibase DA (data availability layer).

Membase is responsible for long-term memory management of AI agents, storing historical context, task status, and knowledge data. Internally, it consists of three sub-modules: Link Hub (remote interaction), Config Hub (identity and permission management), and Memory Hub (long-term record storage). The AIP Protocol establishes communication specifications between agents, enabling different AIs to exchange states and collaboratively execute tasks. Unibase DA focuses on high-frequency AI data storage, synchronization, and on-chain verification.

The key difference between this architecture and traditional Web2 data infrastructure is: data is not controlled by a single platform but reconstructs the cognitive foundation of AI through on-chain verification, distributed storage, and an encrypted memory layer. The synergy of the three constitutes a complete decentralized data network—data generation, storage, indexing, invocation, and verification are all completed in a decentralized environment.

Data Collection and Storage: From Dialogue to Persistent Memory

Trigger Mechanism of Data Collection

In Unibase's architecture, data collection is not passive recording but actively triggered with every interaction of the AI agent. When an AI agent interacts with users, executes tasks, or calls tools, related states are automatically converted into structured memory data. These data may include historical conversations, task results, environmental information, or knowledge fragments.

Unlike traditional centralized systems that indiscriminately store all interaction data in a single database, Unibase's data collection follows a context-driven layered logic. Agents filter and classify information based on task requirements—high-frequency interaction data enters the hot storage path, while long-term knowledge enters the persistent memory layer. This design avoids the blindness of data collection and reduces storage redundancy.

Dual-Layer Design of Storage Architecture

Unibase's storage is not a single system but a dual-layer architecture composed of the AI-native storage layer and Unibase DA.

The AI-native storage layer is a decentralized storage layer built to meet the high-intensity storage demands of AI agents and models. Its core capabilities include:

• High-performance data access: Optimized for AI inference and training workloads, supporting low-latency and high-throughput read/write, with throughput reaching 100 GB/s.
• Massive scalability: Capable of handling exabyte-level data scales, horizontally scaling to millions of storage nodes.
• Programmability: Customizable access control, lifecycle rules, and data governance through smart contracts.
• Data assetization: Treats stored data as on-chain assets, supporting tokenization, trading, and monetization.

Unibase DA provides data availability assurance on top of this. Data is split into fragments via Reed-Solomon encoding and distributed across multiple nodes. Users submit blob commitments and RS parameters on-chain; the data is divided into encoded fragments and distributed to storage nodes. This mechanism ensures that even if some nodes go offline, the data can still be fully recovered.

Compared to traditional centralized storage, Unibase's storage architecture achieves a decoupling of storage and verification—data does not need to trust any single storage node, but rather, distributed redundancy and on-chain verification jointly ensure data persistence and integrity.

Decentralized Indexing System: Making Memory Retrievable

Data storage is only the foundation; enabling efficient data retrieval is the key capability of a decentralized data network. Unibase's indexing system is not an independent search engine but is embedded within the core functions of Membase.

Index Generation Mechanism

When an AI agent writes memory data into Membase, the system simultaneously creates a retrievable index. This process involves two levels:

Structured indexing: For structured data such as task status, configuration parameters, and identity information, Membase establishes key-value indexing through Config Hub and Memory Hub, supporting precise queries.

Semantic indexing: For unstructured data such as conversation history and knowledge fragments, the system establishes semantic indexing through vectorization processing. AI agents in subsequent tasks can retrieve related memory based on semantic similarity, rather than relying solely on exact keyword matching.

Cross-Agent Index Sharing

The unique value of decentralized indexing lies in its shareability across agents. In traditional systems, each AI's memory index is isolated. In Unibase, through the AIP Protocol, different agents can access a shared memory space. This means one agent can learn from, reference, or even form task-oriented intelligent groups with another agent's knowledge.

Index sharing is not unrestricted full openness. The AIP Protocol establishes agent identities through the on-chain agent identity layer; each agent's identity, permissions, and configuration are managed by Config Hub. Index access is doubly constrained by identity authentication and permission control, ensuring data sovereignty is not violated.

Index Update and Invalidation

Indices in a decentralized environment face a core challenge: how to ensure real-time and consistency of indices? Unibase adopts an optimistic verification model—index updates are assumed valid unless challenged. When a missing or incorrect index proof is detected, anyone can verify it off-chain and initiate an on-chain challenge. This mechanism ensures index trustworthiness while avoiding the high gas costs of frequent on-chain verification.

AI Data Invocation Logic: From Storage to Agent Workflow

The ultimate purpose of data collection, storage, and indexing is to support efficient data invocation by AI agents. Unibase's data invocation logic consists of three steps: retrieval, verification, and execution.

Multimodal Retrieval Paths

AI agent data invocation does not follow a single path but selects different retrieval methods based on data type and task requirements:

• Precise retrieval: For deterministic data such as identity information and configuration parameters, directly read via Config Hub's key-value index.
• Semantic retrieval: For knowledge fragments and historical conversations, perform similarity matching retrieval via Memory Hub's vector index.
• Real-time streaming read: For high-frequency updated task status and environmental information, achieve low-latency read through Unibase DA's high-throughput channel.

Pre-Invocation Verification with Zero-Knowledge Proofs

Before returning data to the AI agent, Unibase performs a layer of verification—all memory entries are verified with zero-knowledge proofs (ZK-SNARK) upon writing. When an agent invokes data, the system verifies the zero-knowledge proof of the read data, ensuring the data has not been tampered with during storage.

This design allows AI agents to trust the invoked data without trusting the storage node. This is particularly important for cross-agent collaboration scenarios—Agent A can verify whether the memory shared by Agent B is authentic without relying on trust in Agent B.

Workflow Closed Loop Triggered by Invocation

Data invocation is not the end but the starting point of a new round of data collection. After an AI agent reads historical memory and executes tasks accordingly, new interaction states are again collected, stored, and indexed. This closed loop enables AI agents to continuously accumulate experience rather than starting from scratch each time.

In traditional AI systems, this closed loop is limited by context window length and centralized database access bottlenecks. Unibase, through its decentralized memory layer and high-throughput data availability layer, makes long-term state synchronization possible.

Data Trustworthiness and Verification Mechanism: The Foundation of Trust

The core proposition of a decentralized data network is: how to ensure data authenticity and integrity without relying on centralized trust anchors? Unibase answers this through a multi-layer verification mechanism.

Zero-Knowledge Proof-Driven Storage Proof

Every memory storage in Unibase comes with a zero-knowledge proof. Specifically:

When data is written into Membase, the system generates a cryptographic proof of the data. This proof can verify the data's authenticity and integrity without revealing the data content. Any third party—whether another AI agent, a user, or an on-chain verifier—can verify the proof without accessing the original data.

Dual Guarantees of Encoding Proof and Duality Proof

At the Unibase DA level, data availability verification is achieved through two proof mechanisms:

Encoding proof: Verifies the correctness of Reed-Solomon encoding. This proof is completed directly on-chain, ensuring data is not tampered with during encoding and sharding.

Duality proof: Proves that data remains continuously available within its committed validity window. Storage nodes must submit periodic proofs confirming they still hold the assigned data shards.

These two proofs together constitute a dual guarantee of "correct when written + continuously available during storage."

Optimistic Verification and "One Honest Node" Security Model

Unibase adopts an optimistic verification model to balance security and efficiency. In this model, proofs are assumed valid unless challenged. If a missing or incorrect proof is detected:

• Anyone can verify the proof off-chain.
• If verification fails, an on-chain challenge can be initiated.

The core of this security model is: only one honest verifier is needed to ensure system integrity. Compared to traditional models that rely on a majority of honest verifiers, this design significantly lowers the security assumption threshold.

Trust Anchoring in the Identity Layer

Data trustworthiness depends not only on storage verification but also on the trustworthiness of the data source. Unibase establishes a verifiable identity for each AI agent through the on-chain agent identity layer. Every data write is associated with a specific agent identity and can be traced on-chain.

This mechanism extends data trustworthiness from "data has not been tampered with" to "data comes from a trusted source." In an open agent internet, agents can establish trust relationships by verifying each other's identities and data proofs without relying on centralized identity providers.

Market Data and Ecosystem Progress

As of July 1, 2026 (Beijing time), according to Gate market data, the market performance of UB (Unibase) is as follows:

| Indicator | Data | | --- | --- | | Price | $0.08317 | | Market Cap | $207 million | | 24h High | $0.12690 | | 24h Low | $0.08156 | | 24h Volume | $52.23M | | Total Supply | 10B | | Market Sentiment | Neutral |

Price Performance: UB today at $0.08317, market share 0.035%. Change in the past 24 hours: -22.56%, past 7 days: +19.83%, past 30 days: -53.90%, past 1 year: +429.16%.

Historical Price Range: All-time high $0.243023 (May 15, 2026), all-time low $0.010299 (September 12, 2025). Recent price volatility is high; on June 30, it touched a high of $0.12, with a 24-hour increase of 43.47%.

Ecosystem Progress: Unibase has launched on BNB Chain mainnet, with SDK, documentation, and Explorer fully released. It has integrated frameworks such as MCP, ElizaOS, Virtuals, and Swarms, with over 1,000 agent interactions recorded via the Unibase SDK. Ecosystem projects include BitAgent, TradingFlow, TwinX, Beeper, etc.

Conclusion

Unibase's architecture design demonstrates a clear path: introducing Web3's decentralized concepts into AI data infrastructure. From Membase's long-term memory management, to AIP Protocol's cross-agent communication, to Unibase DA's high-throughput data availability, the three modules together form a complete decentralized data network.

This system attempts to solve three fundamental bottlenecks of traditional AI systems: stateless memory, lack of interoperability, and loss of data sovereignty. Through zero-knowledge proof-driven storage proofs, optimistic verification, and the "one honest node" security model, Unibase establishes a verifiable data trust mechanism in a decentralized environment.

The current AI infrastructure track is still in its early stages, with most projects focusing resources on model inference and computing power. Unibase chooses a differentiated path—focusing on AI's "memory" and "collaboration" capabilities. Whether this choice can build a moat in long-term competition depends on whether the decentralized memory layer can truly become the standardized infrastructure for the AI agent ecosystem.

For practitioners concerned with blockchain data infrastructure, Unibase provides a sample worth continuous tracking—it is not only an experiment in technical architecture but also a systematic answer to the question "What kind of data infrastructure does AI need?"

FAQ

Q1: What is the core difference between Unibase and traditional cloud storage (e.g., AWS S3)?

Traditional cloud storage is a centralized data warehouse, with data controlled by a single entity. Unibase is a decentralized AI memory layer, where data integrity is ensured through distributed storage and on-chain verification, and it is specifically optimized for long-term memory and cross-platform collaboration of AI agents.

Q2: How is the 100 GB/s throughput of Unibase DA achieved?

Unibase DA achieves high throughput through efficient off-chain encoding (Reed-Solomon encoding performance reaches 100 MB/s), an optimistic verification model (on-chain computation is triggered only during fraud detection), and a horizontally scalable architecture (can scale to millions of storage nodes).

Q3: How does an AI agent verify that data read from Unibase has not been tampered with?

Each memory write is accompanied by a zero-knowledge proof. When an agent reads data, it can verify this proof, confirming that the data has not been tampered with during storage, without needing to trust any single storage node.

Q4: What does Unibase's "one honest node" security model mean?

Unlike traditional models that rely on a majority of honest verifiers, Unibase's security model requires only one honest verifier to ensure system integrity. This significantly lowers the security assumption threshold, allowing the system to remain trustworthy even when some nodes behave maliciously.

Q5: What is the main use of the UB token in the Unibase network?

UB is used for paying protocol fees (agent deployment, memory storage, AIP protocol usage), governance voting (locking UB to participate in governance and reward distribution decisions), agent staking (staking UB to activate and promote agents), and knowledge mining (earning UB rewards by contributing prompts, memory, and reusable knowledge).