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

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())

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"]])

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"]])

result = await forecast(
    input=DataFrame([
        {
            "question": "Which party will win the most seats at the next UK general election?",
            "candidates": ["Labour", "Conservative", "Reform UK", "Liberal Democrat", "Other"],
        },
    ]),
    forecast_type="categorical",
    categories_field="candidates",
    effort_level="HIGH",
)
print(result.data[["probabilities", "rationale"]])

result = await forecast(
    input=DataFrame([
        {
            "question": "What will the price of Brent crude oil be on December 31, 2026?",
            "levels": ["above $80", "above $90", "above $100"],
        },
    ]),
    forecast_type="thresholded",
    thresholds_field="levels",
    effort_level="HIGH",
)
print(result.data[["probabilities", "rationale"]])

result = await forecast(
    input=DataFrame([
        {"question": "What will Nvidia's one-day stock return be the day after its next earnings report?"},
    ]),
    forecast_type="numeric",
    output_field="stock_return",
    units="percent",
    condition="Nvidia's next quarterly revenue comes in above $80.07B",
    effort_level="HIGH",
)
print(result.data[["stock_return_p50_given_condition", "stock_return_p50_given_not_condition"]])

from futuresearch import create_session

async with create_session(name="My Session") as session:
    print(f"View session at: {session.get_url()}")

from futuresearch import create_session
from futuresearch.ops import rank_async

async with create_session(name="Async Ranking") as session:
    task = await rank_async(
        session=session,
        task="Score this organization",
        input=dataframe,
        field_name="score",
    )
    print(f"Task ID: {task.task_id}")  # Print this! Useful if your script crashes.
    # Do other stuff...
    result = await task.await_result()

from futuresearch import fetch_task_data

# Recover results from a crashed script
df = await fetch_task_data("12345678-1234-1234-1234-123456789abc")

gemini --version
gemini extensions install https://github.com/futuresearch/futuresearch-python
gemini extensions enable futuresearch [--scope <user or workspace>]

/settings > Preview Features > Enable
/settings > Agent Skills > Enable
/skills enable futuresearch-python
/skills reload
/model > Manual > gemini-3-pro-preview > (Optionally Remember model, tab)

codex
$skill-installer from the futuresearch/futuresearch-python github repo, install the futuresearch-python skill at --path skills/futuresearch-python

python ~/.codex/skills/.system/skill-installer/scripts/install-skill-from-github.py \
  --repo futuresearch/futuresearch-python --path skills/futuresearch-python

1. Open Cursor Settings → Rules
2. In the Project Rules section, click Add Rule
3. Select Remote Rule (Github)
4. Enter: https://github.com/futuresearch/futuresearch-python.git

pip install futuresearch

uv pip install -e .
uv sync
uv sync --group case-studies  # for notebooks

import asyncio
import pandas as pd
from futuresearch.ops import classify

companies = pd.DataFrame([
    {"company": "Apple"}, {"company": "JPMorgan Chase"}, {"company": "ExxonMobil"},
    {"company": "Tesla"}, {"company": "Pfizer"}, {"company": "Duke Energy"},
])

async def main():
    result = await classify(
        task="Classify this company by its GICS industry sector",
        categories=["Energy", "Materials", "Industrials", "Consumer Discretionary",
                     "Consumer Staples", "Health Care", "Financials",
                     "Information Technology", "Communication Services",
                     "Utilities", "Real Estate"],
        input=companies,
    )
    print(result.data[["company", "classification"]])

asyncio.run(main())

uv sync
lefthook install

uv run pytest                                          # unit tests
uv run --env-file .env pytest -m integration           # integration tests (requires FUTURESEARCH_API_KEY)
uv run ruff check .                                    # lint
uv run ruff format .                                   # format
uv run basedpyright                                    # type check
./generate_openapi.sh                                  # regenerate client

@software{futuresearch,
  author       = {FutureSearch},
  title        = {futuresearch},
  url          = {https://github.com/futuresearch/futuresearch-python},
  version      = {0.22.0},
  year         = {2026},
  license      = {MIT}
}