Kibble DeFAI
Last updated
Last updated
DeFAI, as seen in Kibble DeFai, is an AI-enhanced evolution of decentralized finance (DeFi) that simplifies and optimizes blockchain-based financial interactions. It combines DeFi’s core principles—decentralization, transparency, and accessibility—with artificial intelligence to automate complex tasks.
DeFAI is made up of two main parts: the Intent Recognition Engine and the Intent Execution Engine. It uses natural language processing (NLP) and models like OpenAI GPT-4, DeepSeek-Coder-V2, or LLaMA 3.1 70B to understand commands like "Swap 100 USDC to ETH" and then uses few-shot learning and RAG to make sure the commands are carried out correctly. The Intent Execution Engine then carries out intents through solvers for tasks like swaps, bridges, and DeFi.
AI Trading Assistant: Enables trading via simple natural language commands (NLP), suggests smart strategies based on market data, and integrates with Telegram Bot or Web App for easy use.
AI-Driven Liquidity Management: Looks at on-chain data to suggest high-profit liquidity pools, predicts and reduces temporary loss, and maximizes LP fees by allocating capital in a flexible way.
AI Market Insights: Aggregates blockchain data to analyze price and volume trends, predicts token price movements using machine learning, and alerts users to risks like rug pulls or bot trading.
AI Smart Order Routing (SOR): Finds the best prices across DEXs on a single blockchain, reduces slippage by splitting trades across pools, and optimizes execution by predicting market depth.
Users enter commands such as "I want to swap 100 USDC to ETH on Avalanche".
Kibble uses The Intent Recognition Engine to analyze commands and extract key parameters.
The Intent Recognition Engine
The Intent Recognition Engine is powered by Kibble AI models. Modern architectures like transformers could be used to build these models. They could be based on OpenAI's GPT-4, DeepSeek-Coder-V2, or LLaMA 3.1 70B for ideas. These models can understand Web3 terms (like "DEX," "LP," and "gas fees") and the context of blockchain finance after some changes are made to a set of DeFi-specific data that includes transaction logs, smart contract interactions, and user prompts.
Natural Language Processing (NLP) Techniques
NLP forms the foundation of the engine’s ability to process human language. The process involves several key steps:
Tokenization: Splits a command into individual units (e.g., "Swap 100 USDC to ETH" becomes {"Swap", "100", "USDC", "to", "ETH"}.
Named Entity Recognition (NER): Identifies critical entities like "100 USDC" (amount and token), "ETH" (target token), and "Avalanche" (network), tagging them for further processing.
Part-of-Speech Tagging and Dependency Parsing: Determines the grammatical structure and relationships (e.g., "to" links "USDC" and "ETH" as part of a swap action).
Intent Classification: Maps the processed input to a predefined action category (e.g., "swap," "bridge," "deposit"), often using a classifier trained on labeled examples.
Few-Shot Learning for Adaptability
To handle the diverse and evolving nature of user inputs, the engine employs few-shot learning, a technique that allows the AI to generalize from a small number of examples. A handful of sample prompts, like these, might prime the model during inference. Example: From the command "I want to swap 100 USDC to ETH on Avalanche" AI extracts:
“action” : Swap
“token” : USDC
“amount”: 100
“target”: ETH
“network”: Avalanche
From the command "Bridge 50 USDC to Polygon"
“action” : bridge
“Token” : USDC
“amount”: 50
“target”: USDC
“network”: Polygon
With these examples, the engine can infer intent from new commands like "Cross Chain swap 1 ETH to USDC to Optimism," even if it hasn’t encountered that exact phrasing before. This adaptability is crucial in DeFi, where users might use synonyms (e.g., "move" for "bridge") or unconventional phrasing.
3. Enhancing Precision with Context
Retrieval Augmented Generation (RAG) would let the AI ask for real-time Web3 data, like current token prices, network conditions, or liquidity pool stats. This approach would make sure that intent recognition is based on accurate context. For example, interpreting "Swap 100 AVAX for USDC at the best rate" might involve retrieving exchange rates from multiple DEXs to confirm feasibility before finalizing the intent.
Based on the identified intent, Kibble selects the appropriate solver: Intent Execution Engine
The Intent Execution Engine is the system's main part that turns structured intents, which are made by the Intent Recognition Engine, into blockchain transactions and data outputs that can be used. It processes commands like { "action": "swap", "amount": "100", "token": "USDC", "target_token": "ETH", "network": "Avalanche" } executing them efficiently using a modular framework of specialized solvers: Swap Solvers, Bridge Solvers, and Kibble Solvers.
Swap Solvers: Executing Token Swaps
The Kibble Meta-aggregator manages token trades on decentralized exchanges (DEXs).
Rate Optimization: Queries integrated DEXs (e.g., via 0x or Odos APIs) to find the best exchange rate and liquidity for the swap.
Slippage Reduction: Splits large trades (e.g., "Swap 500,000 USDT for USDC") across multiple pools to minimize price impact.
Gas Estimation: Calculates and adjusts gas fees based on network conditions (e.g., Avalanche gas prices).
Transaction Building: Constructs the swap transaction, including smart contract calls, and prompts the user for a wallet signature.
Example: For {"action": "swap", "amount": "100", "token": "AVAX", "target_token": "USDC"}, it might route 60 AVAX through Trader Joe and 40 AVAX through Pangolin for optimal execution.
2. Cross-chain Solvers: Supporting Cross-Chain Transfers
Facilitates asset transfers between blockchains using Kibble Router Cross-chain
Bridge Selection: Chooses the best bridge based on speed, cost, and token compatibility (e.g., USDC support between Avalanche and BSC).
Fee Management: Optimizes bridging and gas fees across both chains.
Transfer Execution: Locks assets on the source chain and releases them on the target chain, coordinating multi-step transactions.
User Interaction: Prepares transactions for user approval at each stage.
Example: For {"action": "bridge", "amount": "200", "token": "USDC", "from_chain": "Avalanche", "to_chain": "BSC"}, it uses Stargate to transfer 200 USDC, ensuring minimal cost.
3. Kibble Solvers: Managing DeFi Tasks, Data Retrieval, Wallet Management, and Balance Tracking
DeFi Task Execution: This part handles interactions with DeFi protocols like Trader Joe, BENQI, AAVE, or Lido. This lets you do things like stake, lend, or provide liquidity.
Protocol Selection: Identifies the target protocol and its smart contract interface (e.g., Lido’s staking contract).
Transaction Preparation: Constructs blockchain transactions (e.g., calling deposits with ETH), handles approvals (e.g., USDC for BENQI), and optimizes gas fees.
Execution: Submits the transaction to the blockchain after user confirmation via wallet signature.
Example: For {"action": "deposit", "amount": "all", "token": "ETH", "protocol": "Lido"}, it stakes the user’s full ETH balance into Lido, returning "Deposited 1.5 ETH into Lido."
2. Data Retrieval:
Fetches and aggregates real-time market data to provide actionable insights without requiring blockchain transactions.
Data Sourcing: Queries on-chain data (e.g., liquidity reserves via LFJ subgraph) and off-chain APIs (e.g., CoinMarketCap, CoinGecko) for metrics like token prices, total value locked (TVL), and annual percentage yields (APY).
Aggregation: Formats data into a user-friendly output.
Delivery: Returns results instantly for user consumption.
Example: For {"action": "get_market_data", "token": "AVAX"}, it delivers "AVAX price: $25, TVL: $500M, APY on BENQI: 7%."
3. Wallet & Balance Management:
Oversees wallet operations and tracks asset balances across tokens and networks, enhancing user control over their portfolio.
Wallet Operations: Manages wallet connections (e.g., MetaMask, Kibble’s Web2-based wallet via Telegram/X), facilitates transaction signing (e.g., "Transfer 10 USDC to vitalik.eth"), and resolves addresses (e.g., ENS).
Balance Tracking: Queries blockchain nodes (e.g., via balanceOf for ERC-20 tokens) to monitor balances across chains (e.g., Avalanche, BSC), including staked or pending assets.
Execution: Prepares transactions or returns balance data, with user approval for transfers.
Example: For {"action": "check_balance", "wallet": "0x123..."}, it returns "Balance: 50 USDC, 1.2 ETH (Avalanche), 0.5 BNB (BSC)."
Results may include:
A transaction is pending user signature.
Text-based answers to queries, such as explanations of DeFi protocols.
The platform provides real-time market data, such as the current AVAX
