How ItWorks
The first AI memory agent with dual-blockchain architecture
Dual-Blockchain Architecture
This application combines two powerful blockchain networks to create an intelligent, transparent memory system that's both cost-effective and verifiable.
Kinic/IC
Storage Layer
- •Stores full content (notes, documents, conversations)
- •Generates vector embeddings for semantic search
- •Finds content by meaning, not just keywords
- •Cost: ~$0.000001 per operation
Monad
Metadata & Audit Layer
- •Logs human-readable metadata (title, summary, tags)
- •Creates searchable, verifiable audit trail
- •Stores content hashes for integrity verification
- •Cost: ~$0.01-0.10 per transaction
Benefits to Monad Users
⚡High-Speed Transactions
Monad's 10,000+ TPS means instant memory logging without waiting for slow block times. Your knowledge operations are confirmed in seconds, not minutes.
💰Low Gas Costs
Monad's efficient parallel execution means affordable on-chain logging. Store rich metadata (titles, summaries, tags) for ~$0.01-0.10 per operation.
📖Public Knowledge Graph
Unlike typical blockchains that only store hashes, we log human-readable metadata on Monad. Browse your knowledge history directly on-chain via block explorers.
🔍Verifiable Transparency
Every insert, search, and chat operation creates an immutable record on Monad. Prove what you knew and when you knew it - perfect for research, compliance, and IP protection.
🛠️EVM Compatibility
Standard Solidity smart contracts mean easy integration with existing Ethereum tools. Query memories via web3.js, ethers.js, or any Monad RPC endpoint.
🌐Decentralized AI
Your AI memory context lives on-chain, not in a centralized database. Monad's performance makes decentralized AI practical for the first time.
How Data Flows
Inserting a Memory
- 1.You submit content (note, research, document)
- 2.AI extracts metadata: title, summary, tags, content hash
- 3.Parallel operations:
- →Kinic/IC: Stores full content + vector embeddings
- →Monad: Logs metadata on-chain (title, summary, tags, hash)
- 4.Returns: memory ID + blockchain transaction hash
Searching Memories
- 1.You enter search query
- 2.Kinic/IC: Converts query to vector, finds similar content by meaning
- 3.Monad: Logs search operation with query metadata
- 4.Returns: ranked results by semantic similarity
AI Chat
- 1.You ask a question
- 2.System searches for relevant memories (semantic search)
- 3.Claude AI receives question + memory context
- 4.AI generates response based on your stored knowledge
- 5.Monad: Logs conversation for audit trail
Why This Matters
For Researchers: Prove your discoveries and thought processes with timestamped, verifiable records on Monad blockchain.
For Developers: Build on top of this memory layer. Query the Monad smart contract to access public knowledge graphs and audit trails.
For Organizations: Compliance-ready documentation. Every knowledge operation is logged on-chain with human-readable metadata.
For AI Enthusiasts: See how decentralized AI memory works. Context lives on-chain, not in centralized databases.
For Monad Community: Demonstrates Monad's unique advantages - high throughput enables rich on-chain data that's impractical on slower chains.