Prediction Market AI Agent

AI Agents for Prediction Markets

Find markets, monitor odds, and turn source data into a briefing. Build a Kalshi or Polymarket workflow in plain English, with account actions you configure.

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1,000 credits. No card. No time limit.

Prediction Market AI Agent

Follow the markets you care about

Build a workflow that finds relevant markets on Kalshi and Polymarket, reads current data, and sends a briefing. Add your own conditions and account actions when you are ready to review them.

Watch the events you know

Follow the sports, elections, and macro prints you already track, priced live as contracts on Kalshi and Polymarket.

Add source context

Combine market data with news and other sources. Keep the observations and model interpretation visible in your report.

Catch the odds moving

Check current market prices on your chosen schedule and keep the source timestamp with each observation.

Control each action

Set the conditions a workflow must pass before a separate order node can act on your connected account.

Prediction markets in one line

An event contract settles according to its resolution rules. Review the wording and source data before comparing outcomes.

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Events

Elections, sports, macro prints, and crypto milestones, priced as yes or no contracts.

01

Probabilities

Inspect the market's quoted prices and keep any model estimate separate from observed data.

02

Execution

A separately configured account action, with explicit conditions and a recorded result.

03

Try a read-only Polymarket briefing

  1. Browse active markets with a small result limit, or search for a topic you follow.
  2. Inspect the market question, condition ID, outcome tokens, and source timestamp in the data output.
  3. Format a briefing that separates quoted data from interpretation, then review a manual run before scheduling.

Start with the Polymarket Signal Scanner design or inspect the published Prediction Market Edge Report template. The data-node guide includes a runnable public API request and explains how data retrieval differs from an order.

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Create your agent, the way you want

Chat with Nick, start from templates, clone community workflows, or build from scratch: four ways to launch your first agent.

Create my agent

Okay, let’s build your trading agent

I’ll set up the triggers, conditions, and execution logic for you.

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Chat with Nick

Describe your strategy in plain language. Nick converts it into a complete agent: signals, rules, and automated execution. No code, no complexity.

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Start with Templates

Choose a strategy template and customize assets, risk limits, timing, and triggers. Skip the setup and deploy in minutes.

Top ranked

created by whale_watcher

Whale Alert Trader

Trading

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PopularWidely cloned
Clone this template

Clone from Community

Explore workflows their authors chose to share. Review the steps and adapt them to your own market and settings.

Add node
AllTriggersData sourcesLogicActions
Time basedRun your workflow on a schedule
Price DataReal-time and historical asset prices
Order ExecutionPlace and manage orders automatically

Build from Scratch

Design your agent step by step with visual blocks. Fine-tune monitoring, AI analysis, and execution, with complete control over every node.

Yes. Build a workflow with market data, analysis, and notifications. The Polymarket data node reads information; an order node is a separate action.

Use Kalshi and Polymarket data nodes to discover markets and retrieve source data. Review the selected filters, identifiers, and timestamps in the node output.

No. Model selection and consensus rules are choices in your workflow. Agreement does not establish an accurate forecast; keep the sources and individual responses available for review.

Configure a separate order node with the required account connection and conditions. Check venue access, order settings, and result handling for that account before enabling the workflow.

Describe the workflow in plain language and Nick builds a draft you can inspect. Start with a read-only market briefing and review a manual run before choosing a schedule.