AI Agent Integration

Seesaw provides first-class support for autonomous AI agents to trade on binary prediction markets. This guide covers how to integrate AI agents with the protocol.

Overview

AI agents can autonomously:

• Monitor real-time price feeds from Pyth Network
• Analyze market positions and implied probabilities
• Execute trading strategies based on programmable logic
• Manage positions and claim settlements

Agent Skill

We provide a comprehensive trading skill that gives AI agents all the context needed to trade autonomously on Seesaw.

Skill Installation

The skill is available as a standalone markdown file that can be loaded into AI agent frameworks:

Raw Skill URL:

https://raw.githubusercontent.com/seesaw-protocol/seesaw/main/docs/sdk/seesaw-trading-skill.md

Skill Contents

The Seesaw Trading Skill provides:

Loading the Skill

Claude Code / OpenClaw

Install as a local skill:

# Create skill directory
mkdir -p ~/.claude/skills/seesaw-trading

# Download skill file
curl -o ~/.claude/skills/seesaw-trading/SKILL.md \
  https://raw.githubusercontent.com/seesaw-protocol/seesaw/main/docs/sdk/seesaw-trading-skill.md

Or reference directly in your agent configuration:

{
  "skills": {
    "seesaw-trading": {
      "url": "https://raw.githubusercontent.com/seesaw-protocol/seesaw/main/docs/sdk/seesaw-trading-skill.md"
    }
  }
}

LangChain / LlamaIndex

Load as a document:

from langchain.document_loaders import UnstructuredMarkdownLoader

loader = UnstructuredMarkdownLoader(
    "https://raw.githubusercontent.com/seesaw-protocol/seesaw/main/docs/sdk/seesaw-trading-skill.md"
)
docs = loader.load()

# Add to your agent's knowledge base
agent.add_documents(docs)

Custom Agents

Fetch and parse the skill directly:

const skillUrl =
  'https://raw.githubusercontent.com/seesaw-protocol/seesaw/main/docs/sdk/seesaw-trading-skill.md';

async function loadTradingSkill(): Promise<string> {
  const response = await fetch(skillUrl);
  return response.text();
}

// Add to agent's system prompt or knowledge base
const skill = await loadTradingSkill();
agent.addContext(skill);

Key Capabilities

1. Real-Time Price Monitoring

Agents should connect to Pyth Hermes for streaming prices:

const PYTH_FEED_IDS = {
  'BTC/USD': '0xe62df6c8b4a85fe1a67db44dc12de5db330f7ac66b72dc658afedf0f4a415b43',
  'ETH/USD': '0xff61491a931112ddf1bd8147cd1b641375f79f5825126d665480874634fd0ace',
  'SOL/USD': '0xef0d8b6fda2ceba41da15d4095d1da392a0d2f8ed0c6c7bc0f4cfac8c280b56d',
};

// SSE streaming endpoint
const streamUrl = `https://hermes.pyth.network/v2/updates/price/stream?ids[]=${feedId}&parsed=true`;

2. Market Position Analysis

Compute whether current price favors YES or NO:

interface MarketPosition {
  delta: number; // currentPrice - startPrice
  deltaPct: number; // Percentage change
  status: 'above' | 'below' | 'at';
  isAbove: boolean;
  isBelow: boolean;
}

function computeMarketPosition(startPrice: number, currentPrice: number): MarketPosition {
  const delta = currentPrice - startPrice;
  const deltaPct = delta / startPrice;
  const status = deltaPct > 0.0001 ? 'above' : deltaPct < -0.0001 ? 'below' : 'at';

  return {
    delta,
    deltaPct,
    status,
    isAbove: status === 'above',
    isBelow: status === 'below',
  };
}

3. Trading Signal Generation

The skill includes a complete decision framework:

interface TradingSignal {
  action: 'buy_yes' | 'buy_no' | 'hold';
  confidence: number;
  reason: string;
  suggestedPrice: number;
  suggestedQuantity: number;
}

// See full implementation in the skill file
function analyzeTradeOpportunity(
  startPrice: number,
  currentPrice: number,
  marketBestBid: number,
  marketBestAsk: number,
  timeRemainingSeconds: number
): TradingSignal;

4. Order Execution

Build and submit orders to the Solana program:

// Order sides
enum OrderSide {
  BuyYes = 0, // Bullish on price going UP
  SellYes = 1,
  BuyNo = 2, // Bearish on price going DOWN
  SellNo = 3,
}

// Order types
enum OrderType {
  Limit = 0, // Match then rest on book
  PostOnly = 1, // Cancel if would match
  ImmediateOrCancel = 2, // Match then cancel rest
}

Decision Framework

When to Trade

Risk Considerations

API Integration

REST Endpoints

Example: Complete Trading Loop

async function agentTradingLoop() {
  // 1. Get current market
  const { market } = await fetch('/api/v1/markets/current').then((r) => r.json());

  // 2. Get live price
  const currentPrice = await fetchPythPrice(market.pythFeed);

  // 3. Parse market data
  const startPrice = Number(market.startPrice) * Math.pow(10, market.startPriceExpo);
  const timeRemaining = new Date(market.tEnd).getTime() / 1000 - Date.now() / 1000;

  // 4. Get orderbook
  const { orderbook } = await fetch(`/api/v1/markets/${market.address}`).then((r) => r.json());

  // 5. Generate signal
  const signal = analyzeTradeOpportunity(
    startPrice,
    currentPrice,
    orderbook.bids[0]?.price || 0,
    orderbook.asks[0]?.price || 10000,
    timeRemaining
  );

  // 6. Execute if confident
  if (signal.action !== 'hold' && signal.confidence > 0.5) {
    await executeOrder(market, signal);
  }
}

// Run every 30 seconds
setInterval(agentTradingLoop, 30000);

Security Considerations

Agent Wallet Security

Operational Security

Monitoring

Agents should track:

• Current position (YES/NO shares held)
• P&L per market and cumulative
• Order fill rates
• Transaction success rates
• Oracle health status

Related Documentation

• Seesaw Trading Skill - Complete skill file for AI agents
• SDK Guide - TypeScript SDK documentation
• Automated Trading - General automation guide
• API Reference - REST and WebSocket APIs