Product Architecture

MindKit is architected as a modular, extensible, and Web3-native platform that simplifies the integration of AI capabilities into decentralized applications. Its plug-and-play design ensures flexibility, scalability, and developer-friendliness, enabling projects to build intelligent systems tailored to their needs. The architecture is composed of four core layers, each designed to address specific aspects of AI integration in Web3:

1. AI Capability Module Layer

The AI Capability Module Layer provides a suite of standardized, composable AI components that developers can mix and match to create custom intelligent systems. These modules are designed for flexibility and ease of use, enabling rapid deployment without deep AI expertise. Key components include:

• Multi-Role AI Assistants: Pre-built or customizable expert roles (e.g., financial analyst, governance advisor, or game NPC) with domain-specific knowledge and behavioral models. Developers can define roles via configuration templates or fine-tune them for specific use cases.

• Knowledge Base Integration: Allows projects to bind structured data—such as whitepapers, governance documents, or protocol specifications—as callable knowledge sources. Supports both on-chain (e.g., stored on IPFS or Arweave) and off-chain data.

• Long-Term Memory System: Persists user preferences, interaction histories, and contextual data to enable continuous, personalized dialogues and decision-making. Memory is encrypted and user-controlled, ensuring privacy.

• Executor Module: Bridges AI logic with on-chain actions, enabling automated execution of smart contract calls, such as submitting proposals, executing trades, or triggering governance actions.

• Workflow Orchestrator: Supports complex, multi-step workflows with conditional logic, asynchronous task execution, and cross-agent collaboration. Developers can define workflows using intuitive visual tools or programmatic scripts.

2. MindOS Runtime

The MindOS Runtime is the core execution environment that ensures secure, reliable, and verifiable AI operations. It handles request processing, instruction interpretation, and module coordination while maintaining Web3-native trust mechanisms. Key components include:

• Prompt-to-Agent Compiler: Translates natural language or structured inputs into executable workflows and API call sequences, enabling seamless interaction between users and AI agents.

• Wallet Signature Verification System: Ensures all interactions are authorized via cryptographic signatures, aligning with Web3’s trustless ethos. Supports integration with wallets like MetaMask, WalletConnect, and others.

• ChainOps Execution Module: Facilitates cross-chain interoperability by enabling data queries, state updates, and smart contract interactions across multiple blockchains (e.g., Ethereum, Polygon, BNB Chain). Supports both EVM and non-EVM chains through modular adapters.

3. Developer & Integration Toolkit

To lower the barrier to entry, MindKit provides a comprehensive set of developer-friendly tools that streamline integration:

• One-Line Integration CLI/Web UI: Simplifies deployment with a single command or a no-code interface, allowing developers to embed AI capabilities without complex setup.

• Cross-Chain SDKs: Supports EVM-compatible chains (e.g., Ethereum, Arbitrum) and WASM-based environments, with pre-built libraries for seamless integration.

• Plug-and-Play Agent Components: Embeddable widgets and APIs for integrating AI agents into frontends, wallets, or messaging platforms like Discord and Telegram.

• Open-Source Repository: A community-driven library of Agent scripts, configuration templates, and sample workflows, fostering collaboration and rapid prototyping.

4. Data & Memory Layer

The Data & Memory Layer ensures secure, scalable, and decentralized storage for AI-related data, balancing performance with Web3 principles:

• Personal Memory Vault: Encrypted, user-controlled storage for interaction histories, preferences, and contextual data, accessible only via wallet-based authentication.

• DID-Bound Knowledge Snapshots: Links knowledge assets and user preferences to decentralized identifiers (DIDs), enabling portable and verifiable data across platforms.

• Shared Knowledge Graph: A community-driven, callable knowledge base that aggregates insights from projects and users, accessible to all agents for enhanced reasoning.

• Storage Integrations: Supports decentralized storage protocols like Arweave, IPFS, and EigenLayer, with optional off-chain caching for performance optimization.

This modular architecture ensures that MindKit is both developer-friendly and future-proof, capable of scaling with the evolving needs of the Web3 ecosystem.