futuresearch

FutureSearch Python SDK

PyPI version License: MIT Python 3.12+

An API for forecasting and multi-agent research.

FutureSearch provides endpoints that use web research agents at scale, for higher accuracy than web search or single agent approaches alone can achieve. forecast runs a team of forecasters to predict future dates, numbers, and probabilities. multi_agent orchestrates multiple researchers to answer one question. agent_map runs one research agent over every row of a dataset, scaling to thousands of rows and agents.

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.

Endpoints

Role	What it does	Cost	Scales To
forecast()	Predict outcomes	50¢-1.20¢	1k rows
multi_agent()	A team of researchers per for each question	$0.30-$2	1k rows
agent_map()	One researcher per row of a dataset	1–11¢	10k rows
rank()	Research, then score	1-5¢	10k rows
classify()	Research, then categorize	0.1-0.7¢	10k rows
dedupe() and merge()	Find matching rows	0.2-0.5¢	20k rows

See the full API reference, guides, and case studies, (for example, see our case study running a Research task on 10k rows, running agents that used 120k LLM calls.)

Or just ask Claude in your interface of choice:

Find every startup selling training data and evals to frontier AI labs.

Take this 10,000-row CSV of drugs and find the FDA regulatory status of each.

Forecast which of these 500 cancer drug trials are most likely to succeed.

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SDK Examples

from futuresearch.ops import forecast, agent_map, multi_agent
from pandas import DataFrame

# A team of forecasters: research each question, then predict
result = await forecast(
    input=DataFrame([
        {"question": "When will Anthropic IPO?"},
        {"question": "When will OpenAI IPO?"},
    ]),
    forecast_type="date",
)
print(result.data.head())

# One web research agent per row, 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"},
        # ... 100 more rows
    ]),
)
print(result.data.head())

# A team of agents on one question; return_list emits one row per item
result = await multi_agent(
    task="List the most-funded AI infrastructure startups founded since 2023",
    input=DataFrame(),
    return_list=True,
)
print(result.data.head())

See the API docs. Agents are tuned on Deep Research Bench, Bench To the Future, on prediction markets, and in the stock market..

Sessions