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

Label this 5,000 row CSV with the right categories.

Find the rows in this 10,000 row pandas dataframe that represent good opportunities.

Rank these 2,000 people from Wikipedia on who is the most bullish on AI.

from futuresearch.ops import single_agent, agent_map
from pandas import DataFrame
from pydantic import BaseModel

class CompanyInput(BaseModel):
    company: str

# Single input, run one web research agent
result = await single_agent(
    task="Find this company's latest funding round and lead investors",
    input=CompanyInput(company="Anthropic"),
)
print(result.data.head())

# Map input, run a set of web research agents in parallel
result = await agent_map(
    task="Find this company's latest funding round and lead investors",
    input=DataFrame([
        {"company": "Anthropic"},
        {"company": "OpenAI"},
        {"company": "Mistral"},
    ]),
)
print(result.data.head())

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      = {0.8.3},
  year         = {2026},
  license      = {MIT}
}