AI Crypto Research Agent

Multi-agent AI system that orchestrates 12 specialized crypto research agents to analyze tokens, track whales, monitor Twitter sentiment, and deliver real-time market intelligence.

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## Inspiration

Crypto research is fragmented. To analyze a single token, traders must check CoinGecko for prices, DeFiLlama for TVL, Twitter for sentiment, on-chain explorers for whale movements, and news sites for context. I wanted to build a system where you just ask a question and AI agents do all the research for you in parallel.

## What it does

AI Crypto Research Agent is a multi-agent AI research system that orchestrates 12 specialized crypto agents through the Heurist Mesh network:

  • Market Data: CoinGecko (prices, trending), CryptoCompare (OHLCV data)
  • On-Chain Analysis: Solana Token Agent (whale tracking, holder analysis), DeFiLlama (TVL, protocol metrics)
  • Social Intelligence: Elfa Twitter Agent (sentiment, influencer tracking)
  • News & Context: CryptoPanic (aggregated news), ExaSearch (deep web research)
  • Cross-Chain: EVM Token Agent, Uniswap, 1inch for multi-chain coverage

The system uses a 3-step pipeline:

  1. Orchestrator analyzes your question and selects the best agents
  2. Agents execute in parallel with automatic retry on failure
  3. Synthesizer combines all data into a coherent research report

## How I built it

Built entirely with Kiro Code using vibe coding. The project uses:

  • Next.js 15 with React 19 and Server-Sent Events for real-time streaming
  • Heurist Mesh API to access the 12 AI agents
  • TradingView widget for live charts
  • Custom retry logic with exponential backoff
  • IP-based rate limiting to prevent abuse

Kiro's spec-driven development helped me define the architecture upfront, and the steering docs ensured consistent code quality throughout.

## Challenges I ran into

  • Agent parameter inconsistency: Each Heurist agent has different parameter names. Solved by switching from tool mode to query mode for more flexibility.
  • Streaming complexity: Coordinating multiple agent responses in real-time while maintaining UI responsiveness required careful state management.
  • Rate limiting: Balancing between allowing demo usage and preventing API abuse.

## Accomplishments that I'm proud of

  • Built a functional multi-agent system in a weekend
  • 12 agents working in parallel with automatic failover
  • Clean, terminal-style UI that shows the research process in real-time
  • Everything written with Kiro Code - zero manual coding

## What I learned

  • How to orchestrate multiple AI agents effectively
  • The power of spec-driven development with Kiro
  • Real-time streaming patterns with Server-Sent Events
  • The importance of graceful error handling in distributed systems

## What's next for AI Crypto Research Agent

  • Add portfolio tracking
  • Integrate more chains (Base, Arbitrum, etc.)
  • Build a Telegram bot interface
  • Add historical analysis and backtesting

Built With

  • heurist-mesh-api
  • kiro-code
  • next.js-15
  • react-19
  • tailwind-css
  • tradingview-widget
  • typescript
  • vercel
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