FutureSearch Python SDK

An API for frontier forecasting.

FutureSearch predicts the future. Accuracy is verifiable via our public track record on stocks, prediction markets, public benchmarks, and forecasting tournaments.

Try it yourself in the app, or give advanced forecasting and multi-agent capabilities to your AI wherever you use it (Claude.ai, Claude Cowork, Claude Code, or Gemini/Codex/other AI surfaces), or point them to this Python SDK.

Installation

Claude.ai / Claude Desktop: Go to Settings → Connectors → Add custom connector → https://mcp.futuresearch.ai/mcp

Claude Code:

claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp

Then sign in with Google.

Forecasting

forecast() takes a table of questions about the future and returns a forecast for each row, with a rationale column explaining each answer. Five modes cover the shapes a question can take.

Effort level is "LOW" or "HIGH": roughly $0.15 per question at low effort and $2 at high effort. Left unset, a single question runs at high effort and a batch runs at low. Categorical, thresholded, and conditional forecasts always require "HIGH".

Binary

The probability, 0 to 100, that a YES/NO question resolves YES. Output columns: probability and rationale.

import asyncio
from pandas import DataFrame
from futuresearch.ops import forecast

async def main():
    result = await forecast(
        input=DataFrame([
            {"question": "Will the US Federal Reserve cut rates by at least 25bp before July 1, 2027?"},
            {"question": "Will SpaceX land Starship on the Moon before 2030?"},
        ]),
        forecast_type="binary",
    )
    print(result.data[["question", "probability", "rationale"]])

asyncio.run(main())

Numeric

Percentile estimates (p10 through p90) for a continuous quantity. Requires output_field and units.

result = await forecast(
    input=DataFrame([
        {"question": "What will the price of Brent crude oil be on December 31, 2026?"},
    ]),
    forecast_type="numeric",
    output_field="price",
    units="USD per barrel",
)
print(result.data[["price_p10", "price_p50", "price_p90"]])

Date

Percentile dates (p10 through p90, as YYYY-MM-DD) for timing questions. Requires output_field.

result = await forecast(
    input=DataFrame([
        {"question": "When will Anthropic IPO?"},
    ]),
    forecast_type="date",
    output_field="ipo_date",
)
print(result.data[["ipo_date_p10", "ipo_date_p50", "ipo_date_p90"]])

Categorical

Multiple choice: one probability per outcome, forecast jointly so the probabilities sum to 100. Each row holds its own option list in the column named by categories_field. Make the set exhaustive; add an "Other" option when it isn't.

result = await forecast(
    input=

... [View full README on GitHub](https://github.com/futuresearch/everyrow-sdk#readme)