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: the forecaster leads Metaculus's Summer 2026 FutureEval tournament, sits above the superforecaster median on ForecastBench, and holds the best pooled score on BTF-3, our 1,907-question pastcasting benchmark. Those are live standings, so the link carries the current positions. Every forecast draws on a shared world model that reconciles related questions against each other; it improved all nine base forecasters we tested, four of them significantly.

Try it yourself in the app, or give advanced forecasting and multi-agent capabilities to your AI wherever you use it (Claude.ai, 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 the same way you do in the FutureSearch web app and pick the account the connection should use.

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

... View full README on GitHub