Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-futuresearch-futuresearch-mcp": {
"args": [
"futuresearch"
],
"command": "uvx"
}
}
}Are you the author?
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Deploy a team of researchers to forecast, score, classify, or gather data. Use yourself in the app, or give your team of researchers 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.
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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.
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.
| 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.
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=Da
... [View full README on GitHub](https://github.com/futuresearch/everyrow-sdk#readme)