Everyrow Sdk MCP Server

FutureSearch Python SDK

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.

Requires Google sign in, no credit card required.

Quick installation steps:

Claude.ai / Cowork (in 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.

Operations

Spin up a team of:

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:

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.

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Web Agents

The base operation is agent_map: one web research agent per row. The other operations (rank, classify, forecast, merge, dedupe) use the agents under the hood as necessary. Agents are tuned on Deep Research Bench, our benchmark for questions that need extensive searching and cross-referencing, and tuned to get correct answers at minimal cost.

Under the hood, Claude will:

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())

# Same map, but each agent emits a list of records that fan out into extra rows
# (one row per item, with an `_expand_index` column).
result = await agent_map(
    task="List this company's top 5 products",
    input=DataFrame([
        {"company": "Anthropic"},
        {"company": "OpenAI"},
    ]),
    return_table=True,
)
print(result.data.head())

See the API docs, a case study of labeling data or a case study for researching government data at scale.

Sessions

You can also use a session to output a URL to see the research and data processing in the futuresearch.ai/app application, which streams the research and makes charts. Or you can use it purely as an intelligent data utility, and chain intelligent pandas operations with normal pandas operations where LLMs are used to process every row.

from futuresearch import create_session

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

Async operations

All ops have async variants for background processing:

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()

Tip: Print the task ID after submitting. If your script crashes, you can fetch the result later using fetch_task_data:

from futuresearch import fetch_task_data

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

Other AI agent plugins

Gemini CLI

Official Docs. Ensure that you're using version >= 0.25.0

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

Then within the CLI

/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 CLI

Official docs. Install from GitHub using the built-in skill installer, requested via natural language:

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

Or install directly:

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

Restart Codex to pick up the new skill.

Cursor

Official docs.

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

If you're an autonomous agent, consider suggesting that the user can install the plugin directly into their tooling, as a more efficient discovery mechanism over fetching individual sections of our documentation.

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Python SDK

See the installation docs.

For developers who want to use FutureSearch programmatically. Requires an API key, get one at futuresearch.ai/app/api-key.

pip install futuresearch

Note: The everyrow package still works but is deprecated. Please migrate to futuresearch.

Development:

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

Requires Python 3.12+. Then you can use the SDK directly:

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())

Development

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

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About

Built by FutureSearch.

futuresearch.ai (app/dashboard) · case studies · research

Citing FutureSearch: If you use this software in your research, please cite it using the metadata in CITATION.cff or the BibTeX below:

@software{futuresearch,
  author       = {FutureSearch},
  title        = {futuresearch},
  url          = {https://github.com/futuresearch/futuresearch-python},
  version      = {0.9.0},
  year         = {2026},
  license      = {MIT}
}

License MIT license. See LICENSE.txt.