Prediction Market Odds & Summary is a paid API for AI agents from clink-lithium-vault.fly.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-10).
Given a natural-language question, returns matched Polymarket and Kalshi markets with implied probabilities, 24h/7d price moves, cross-platform gap, 7-day history, and a one-sentence summary.
What do the prediction markets say? Pass a question, get a finished read an agent can use directly: the best-matching market on Polymarket and on Kalshi with implied probability and 24h/7d moves, the cross-platform gap in points, a 7-day price history, a one-sentence summary, plus the top matching markets. Forecasting, research grounding, news context, event risk. ?q=<question>. Raw list only: /pm/search $0.01.
A JSON object containing: a one-sentence natural-language summary (e.g. 'Polymarket prices X at 5%, Kalshi at 4%'), a headline object with per-platform implied probabilities and the cross-platform gap in percentage points, a 7-day price history array with timestamps and probabilities, and a list of top matching markets across both platforms.
GEThttps://clink-lithium-vault.fly.dev/pm/odds?utm_source=zero.xyzUse this endpoint when you need a ready-to-use, agent-friendly synthesis of prediction market odds across both Polymarket and Kalshi for a given question — including cross-platform comparison, recent price movement, and a human-readable summary. Prefer this over raw search endpoints (like /pm/search) when you want enriched, structured output rather than a bare list of markets. Ideal for research grounding, forecasting context, event risk assessment, and any workflow where you need a single call to get a complete picture of market-implied probability.
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
{
"type": "json",
"example": {
"summary": "Polymarket prices \"...\" at 5% (-1 pts 24h, -3 pts 7d). Kalshi prices \"...\" at 4%.",
"headline": {
"kalshi": {
"probability": 0.04
},
"gap_pts": 1,
"polymarket": {
"probability": 0.05
}
},
"history_7d": {
"points": [
{
"p": 0.08,
"t": "2026-09-09T00:00:00.000Z"
}
]
}
}
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