Polymarket prediction agents

polymarket-prediction-agents

USE FOR:

• "trade on prediction markets with AI"
• "Polymarket agent / bot"
• "prediction market analysis with LLM"
• "autonomous betting / event trading agent"
• "RAG-powered market research pipeline"
• "crypto prediction market automation" tags: [polymarket, prediction-markets, crypto, DeFi, agents, LLM, RAG, Polygon, CLOB] kind: framework category: crypto-defi-trading

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What Is Polymarket Agents?

Developer framework for building autonomous AI agents that trade on Polymarket — a decentralized prediction market platform on Polygon.

• Repo: https://github.com/Polymarket/agents
• License: MIT
• Runtime: Python 3.9
• Note: US persons and restricted jurisdictions cannot trade per ToS

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Architecture

┌──────────────── DATA LAYER ──────────────────┐
│  Gamma API       → market metadata & events   │
│  News providers  → relevant articles          │
│  Web search      → real-time information      │
│  Betting APIs    → odds & market sentiment    │
└───────────────────────┬──────────────────────┘
                        ↓
┌──────────────── AI LAYER ────────────────────┐
│  LLM (OpenAI / any)  → reasoning             │
│  RAG (Chroma DB)     → vectorized context    │
│  Prompt utilities    → structured queries    │
└───────────────────────┬──────────────────────┘
                        ↓
┌──────────────── EXECUTION LAYER ─────────────┐
│  py-clob-client  → CLOB order generation     │
│  Polygon wallet  → on-chain signing          │
│  Polymarket DEX  → order execution           │
└──────────────────────────────────────────────┘

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Installation

git clone https://github.com/Polymarket/agents.git
cd agents
virtualenv --python=python3.9 .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env

.env Configuration:

POLYGON_WALLET_PRIVATE_KEY="0x..."   # Polygon wallet for trading
OPENAI_API_KEY="sk-..."              # LLM provider
POLYMARKET_API_KEY="..."             # Optional: API key tier

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CLI Usage

# List markets by volume
python scripts/python/cli.py get-all-markets --limit 5 --sort-by volume

# Get specific event
python scripts/python/cli.py get-market --market-id <id>

# Search markets by keyword
python scripts/python/cli.py get-markets --keyword "election" --limit 10

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Trading Execution

# Run autonomous trading agent
python agents/application/trade.py

# The agent will:
# 1. Fetch current markets via Gamma API
# 2. Retrieve relevant news + RAG context
# 3. Query LLM for probability assessment
# 4. Compare to market price → find edge
# 5. Sign + submit order via py-clob-client

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RAG Integration

from agents.utils.chroma import ChromaClient

# Vectorize news articles for retrieval
client = ChromaClient()
client.add_documents(news_articles)

# Query for market-relevant context
results = client.query("US election 2026 polling", n_results=5)

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Prediction Market Edge Formula

Edge = Estimated_Probability - Market_Price

If Edge > threshold → BUY YES / NO token
If Edge < -threshold → SELL or BUY opposite

LLM assesses probability from news + context; market price is the current token price (0–1).

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Key Components

Component	Description
Gamma API	Polymarket's REST API for markets/events data
py-clob-client	Order book client for CLOB trading
Chroma DB	Vector store for RAG-based news retrieval
Prompt utils	LLM prompt engineering helpers
trade.py	Main agent execution loop
cli.py	CLI for market exploration

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Extending the Agent

# Custom analyst agent pattern
class MyPredictionAgent:
    def analyze(self, market: dict) -> float:
        context = self.rag.query(market["question"])
        news = self.news_api.get_recent(market["question"])
        
        prompt = f"""
        Question: {market['question']}
        Context: {context}
        News: {news}
        Current market price: {market['price']}
        
        Estimate the true probability (0-1) and justify.
        """
        response = self.llm.complete(prompt)
        return self.parse_probability(response)

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