futuresearch · dschwarz26 · Feb 27, 2026 · Feb 27, 2026 · Feb 27, 2026 · Feb 27, 2026
diff --git a/.claude-plugin/marketplace.json b/.claude-plugin/marketplace.json
@@ -11,7 +11,7 @@
       "name": "everyrow",
       "source": "./",
       "description": "Give Claude Code a research team. Forecast, score, classify, or research every row of a dataset.",
-      "version": "0.4.0"
+      "version": "0.4.1"
     }
   ]
 }
diff --git a/.claude-plugin/plugin.json b/.claude-plugin/plugin.json
@@ -1,7 +1,7 @@
 {
   "name": "everyrow",
   "description": "Give Claude Code a research team. Forecast, score, classify, or research every row of a dataset.",
-  "version": "0.4.0",
+  "version": "0.4.1",
   "author": {
     "name": "FutureSearch"
   },

diff --git a/README.md b/README.md
@@ -29,7 +29,7 @@ Spin up a team of:
 | [**Agents**](https://everyrow.io/docs/reference/RESEARCH)       | Research, then analyze     | 1–3¢/researcher    | 10k rows |
 | [**Forecasters**](https://everyrow.io/docs/reference/FORECAST)  | Predict outcomes           | 20-50¢/researcher  | 10k rows |
 | [**Scorers**](https://everyrow.io/docs/reference/RANK)          | Research, then score       | 1-5¢/researcher    | 10k rows |
-| [**Classifiers**](https://everyrow.io/docs/reference/SCREEN)    | Research, then categorize  | 0.1-0.7¢/researcher | 10k rows |
+| [**Classifiers**](https://everyrow.io/docs/reference/CLASSIFY)  | Research, then categorize  | 0.1-0.7¢/researcher | 10k rows |
 | [**Matchers**](https://everyrow.io/docs/reference/MERGE)        | Find matching rows         | 0.2-0.5¢/researcher | 20k rows |
 
 See the full [API reference](https://everyrow.io/docs/api), [guides](https://everyrow.io/docs/guides), and [case studies](https://everyrow.io/docs/case-studies), (for example, see our [case study](https://everyrow.io/docs/case-studies/llm-web-research-agents-at-scale) running a `Research` task on 10k rows, running agents that used 120k LLM calls.)
@@ -203,23 +203,23 @@ Requires Python 3.12+. Then you can use the SDK directly:
 ```python
 import asyncio
 import pandas as pd
-from everyrow.ops import screen
-from pydantic import BaseModel, Field
+from everyrow.ops import classify
 
 companies = pd.DataFrame([
-    {"company": "Airtable",}, {"company": "Vercel",}, {"company": "Notion",}
+    {"company": "Apple"}, {"company": "JPMorgan Chase"}, {"company": "ExxonMobil"},
+    {"company": "Tesla"}, {"company": "Pfizer"}, {"company": "Duke Energy"},
 ])
 
-class JobScreenResult(BaseModel):
-    qualifies: bool = Field(description="True if company lists jobs with all criteria")
-
 async def main():
-    result = await screen(
-        task="""Qualifies if: 1. Remote-friendly, 2. Senior, and 3. Discloses salary""",
+    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,
-        response_model=JobScreenResult,
     )
-    print(result.data.head())
+    print(result.data[["company", "classification"]])
 
 asyncio.run(main())
 ```

diff --git a/docs/api.md b/docs/api.md
@@ -1,11 +1,11 @@
 ---
 title: API Reference
-description: Complete API reference for everyrow — screen, rank, dedupe, merge, forecast, and research operations powered by LLM web research agents.
+description: Complete API reference for everyrow — screen, rank, dedupe, merge, classify, forecast, and research operations powered by LLM web research agents.
 ---
 
 # API Reference
 
-Six operations for processing data with LLM-powered web research agents. Each takes a DataFrame and a natural-language instruction.
+Seven operations for processing data with LLM-powered web research agents. Each takes a DataFrame and a natural-language instruction.
 
 ## screen
 
@@ -55,6 +55,20 @@ result = await merge(task=..., left_table=df1, right_table=df2)
 Guides: [Fuzzy Join Without Matching Keys](/docs/fuzzy-join-without-keys)
 Case Studies: [LLM Merging at Scale](/docs/case-studies/llm-powered-merging-at-scale), [Match Software Vendors to Requirements](/docs/case-studies/match-software-vendors-to-requirements)
 
+## classify
+
+```python
+result = await classify(
+    task="Classify each company by its primary industry sector",
+    categories=["Technology", "Finance", "Healthcare", "Energy"],
+    input=companies_df,
+)
+```
+
+`classify` assigns each row in a DataFrame to one of the provided categories using web research that scales to the difficulty of the classification. Supports binary (yes/no) and multi-category classification with optional reasoning output.
+
+[Full reference →](/docs/reference/CLASSIFY)
+
 ## forecast
 
 ```python

diff --git a/docs/case-studies.md b/docs/case-studies.md
@@ -1,12 +1,16 @@
 ---
 title: Case Studies
-description: Runnable case studies demonstrating everyrow operations on real datasets — screen, rank, dedupe, merge, and research with LLM-powered agents.
+description: Runnable case studies demonstrating everyrow operations on real datasets — classify, screen, rank, dedupe, merge, forecast, and research with LLM-powered agents.
 ---
 
 # Case Studies
 
 Runnable case studies with real datasets. Each case study demonstrates an everyrow operation end-to-end with output you can inspect.
 
+## Classify
+
+- [Classify and Label Data](/docs/classify-dataframe-rows-llm)
+
 ## Screen
 
 - [Screen 10,000 Rows](/docs/case-studies/llm-powered-screening-at-scale)
@@ -33,6 +37,10 @@ Runnable case studies with real datasets. Each case study demonstrates an everyr
 - [Link Records Across Medical Datasets](/docs/case-studies/match-clinical-trials-to-papers)
 - [Merge Costs and Speed](/docs/case-studies/understanding-costs-and-speed-for-merge)
 
+## Forecast
+
+- [Automating Forecasting Questions](https://futuresearch.ai/automating-forecasting-questions/)
+
 ## Multi-Method
 
 - [Multi-Stage Lead Qualification](/docs/case-studies/multi-stage-lead-qualification)

diff --git a/docs/getting-started.md b/docs/getting-started.md
@@ -150,13 +150,15 @@ df = await fetch_task_data("12345678-1234-1234-1234-123456789abc")
 
 ## Operations
 
-| Operation                       | Description                                |
-| ------------------------------- | ------------------------------------------ |
-| [Screen](/reference/SCREEN)     | Filter rows by criteria requiring judgment |
-| [Rank](/reference/RANK)         | Score rows by qualitative factors          |
-| [Dedupe](/reference/DEDUPE)     | Deduplicate when fuzzy matching fails      |
-| [Merge](/reference/MERGE)       | Join tables when keys don't match exactly  |
-| [Research](/reference/RESEARCH) | Run web agents to research each row        |
+| Operation                         | Description                                |
+| --------------------------------- | ------------------------------------------ |
+| [Classify](/reference/CLASSIFY)   | Categorize rows into predefined classes    |
+| [Screen](/reference/SCREEN)       | Filter rows by criteria requiring judgment |
+| [Rank](/reference/RANK)           | Score rows by qualitative factors          |
+| [Dedupe](/reference/DEDUPE)       | Deduplicate when fuzzy matching fails      |
+| [Merge](/reference/MERGE)         | Join tables when keys don't match exactly  |
+| [Forecast](/reference/FORECAST)   | Predict probabilities for binary questions |
+| [Research](/reference/RESEARCH)   | Run web agents to research each row        |
 
 ## See Also
 

diff --git a/docs/guides.md b/docs/guides.md
@@ -1,12 +1,16 @@
 ---
 title: Guides
-description: Step-by-step tutorials for using everyrow to screen, rank, dedupe, merge, and research data with LLM-powered agents.
+description: Step-by-step tutorials for using everyrow to classify, screen, rank, dedupe, merge, forecast, and research data with LLM-powered agents.
 ---
 
 # Guides
 
 Practical walkthroughs that show you how to use everyrow for common data processing tasks. Each guide covers a single operation end-to-end with working code.
 
+## Classify
+
+- [Classify and Label Rows](/docs/classify-dataframe-rows-llm)
+
 ## Screen
 
 - [Filter a Dataset Intelligently](/docs/filter-dataframe-with-llm)
@@ -25,8 +29,11 @@ Practical walkthroughs that show you how to use everyrow for common data process
 
 - [Join Tables Without Shared Keys](/docs/fuzzy-join-without-keys)
 
+## Forecast
+
+- [Forecast Binary Questions](/docs/reference/FORECAST)
+
 ## Research
 
 - [Add a Column via Web Research](/docs/add-column-web-lookup)
-- [Classify and Label Rows](/docs/classify-dataframe-rows-llm)
 - [LLM-Powered Data Labeling](/docs/active-learning-llm-oracle)
diff --git a/docs/mcp-server.md b/docs/mcp-server.md
@@ -65,6 +65,29 @@ Join two CSVs using intelligent entity matching (LEFT JOIN semantics).
 
 Returns `task_id` and `session_url`. Call `everyrow_progress` to monitor.
 
+### everyrow_classify
+
+Classify each row into one of the provided categories.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `task` | string | Yes | Classification instructions. |
+| `categories` | list[string] | Yes | Allowed categories (minimum 2). Each row is assigned exactly one. |
+| `classification_field` | string | No | Output column name (default: `"classification"`). |
+| `include_reasoning` | boolean | No | Include a reasoning column (default: false). |
+
+Returns `task_id` and `session_url`. Call `everyrow_progress` to monitor.
+
+### everyrow_forecast
+
+Forecast the probability of binary questions.
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `context` | string | No | Optional batch-level context for all questions. |
+
+Returns `task_id` and `session_url`. Call `everyrow_progress` to monitor.
+
 ### everyrow_agent
 
 Run web research agents on each row.

diff --git a/docs/reference/CLASSIFY.md b/docs/reference/CLASSIFY.md
@@ -0,0 +1,113 @@
+---
+title: classify
+description: API reference for the EveryRow classify tool, which assigns each row of a dataset into one of the provided categories using web research.
+---
+
+# Classify
+
+`classify` takes a DataFrame and a list of allowed categories, then assigns each row to exactly one category using web research that scales to the difficulty of the classification. Supports binary (yes/no) and multi-category classification with optional reasoning output.
+
+## Examples
+
+### GICS sector classification
+
+```python
+from pandas import DataFrame
+from everyrow.ops import classify
+
+companies = DataFrame([
+    {"company": "Apple"},
+    {"company": "JPMorgan Chase"},
+    {"company": "ExxonMobil"},
+    {"company": "Pfizer"},
+    {"company": "Procter & Gamble"},
+    {"company": "Tesla"},
+    {"company": "AT&T"},
+    {"company": "Caterpillar"},
+    {"company": "Duke Energy"},
+    {"company": "Simon Property Group"},
+])
+
+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"]])
+```
+
+Output:
+
+| company              | classification         |
+|----------------------|------------------------|
+| Apple                | Information Technology |
+| JPMorgan Chase       | Financials             |
+| ExxonMobil           | Energy                 |
+| Pfizer               | Health Care            |
+| Procter & Gamble     | Consumer Staples       |
+| Tesla                | Consumer Discretionary |
+| AT&T                 | Communication Services |
+| Caterpillar          | Industrials            |
+| Duke Energy          | Utilities              |
+| Simon Property Group | Real Estate            |
+
+### Binary classification
+
+For yes/no questions, use two categories:
+
+```python
+result = await classify(
+    task="Is this company founder-led?",
+    categories=["yes", "no"],
+    input=companies,
+)
+```
+
+### Custom output column and reasoning
+
+```python
+result = await classify(
+    task="Classify each company by its primary industry sector",
+    categories=["Technology", "Finance", "Healthcare", "Energy"],
+    input=companies,
+    classification_field="sector",
+    include_reasoning=True,
+)
+print(result.data[["company", "sector", "reasoning"]])
+```
+
+## Parameters
+
+| Name | Type | Default | Description |
+|------|------|---------|-------------|
+| `task` | str | required | Natural-language instructions describing how to classify each row |
+| `categories` | list[str] | required | Allowed category values (minimum 2). Each row is assigned exactly one. |
+| `input` | DataFrame | required | Rows to classify |
+| `classification_field` | str | `"classification"` | Name of the output column for the assigned category |
+| `include_reasoning` | bool | `False` | If True, adds a `reasoning` column with the agent's justification |
+| `session` | Session | Optional, auto-created if omitted | |
+
+## Output
+
+One column is added to each input row (name controlled by `classification_field`):
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `classification` | str | One of the provided `categories` values |
+| `reasoning` | str | Agent's justification (only if `include_reasoning=True`) |
+
+## Via MCP
+
+MCP tool: `everyrow_classify`
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `task` | string | Classification instructions |
+| `categories` | list[string] | Allowed categories (minimum 2) |
+| `classification_field` | string | Output column name (default: `"classification"`) |
+| `include_reasoning` | boolean | Include reasoning column (default: false) |
diff --git a/everyrow-mcp/README.md b/everyrow-mcp/README.md
@@ -2,7 +2,7 @@
 
 MCP (Model Context Protocol) server for [everyrow](https://everyrow.io): agent ops at spreadsheet scale.
 
-This server exposes everyrow's 5 core operations as MCP tools, allowing LLM applications to screen, rank, dedupe, merge, and run agents on CSV files.
+This server exposes everyrow's core operations as MCP tools, allowing LLM applications to classify, screen, rank, dedupe, merge, forecast, and run agents on CSV files.
 
 **All tools operate on local CSV files.** Provide absolute file paths as input, and transformed results are written to new CSV files at your specified output path.
 
@@ -116,6 +116,31 @@ Parameters:
 
 Example: Match software products (left, enriched) to parent companies (right, lookup): Photoshop -> Adobe
 
+### everyrow_classify
+
+Classify each row into one of the provided categories.
+
+```
+Parameters:
+- task: Natural language classification instructions
+- categories: Allowed categories (minimum 2)
+- classification_field: (optional) Output column name (default: "classification")
+- include_reasoning: (optional) Include reasoning column (default: false)
+```
+
+Example: Classify companies by GICS sector with categories ["Energy", "Financials", "Information Technology", ...]
+
+### everyrow_forecast
+
+Forecast the probability of binary questions.
+
+```
+Parameters:
+- context: (optional) Batch-level context for all questions
+```
+
+Example: "Will the US Federal Reserve cut rates before July 2027?"
+
 ### everyrow_agent
 
 Run web research agents on each row of a CSV.

diff --git a/everyrow-mcp/manifest.json b/everyrow-mcp/manifest.json
@@ -2,7 +2,7 @@
   "manifest_version": "0.4",
   "name": "everyrow-mcp",
   "display_name": "Everyrow MCP Server",
-  "version": "0.4.0",
+  "version": "0.4.1",
   "description": "Give your AI a research team. Forecast, score, classify, or research every row of a dataset.",
   "long_description": "MCP server for everyrow: give your AI a research team. Each operation dispatches web research agents across a dataset to forecast, score, classify, deduplicate, merge, or research at scale.",
   "author": {
@@ -53,6 +53,10 @@
       "name": "everyrow_forecast",
       "description": "Forecast the probability of binary questions from a CSV file."
     },
+    {
+      "name": "everyrow_classify",
+      "description": "Classify each row of a dataset into one of the provided categories."
+    },
     {
       "name": "everyrow_single_agent",
       "description": "Run a single web research agent on a task, optionally with context data."
@@ -97,7 +101,7 @@
       "python": ">=3.12"
     }
   },
-  "keywords": ["everyrow", "dataframe", "csv", "ai", "data-processing", "dedupe", "merge", "rank", "screen"],
+  "keywords": ["everyrow", "dataframe", "csv", "ai", "data-processing", "classify", "dedupe", "merge", "rank", "screen", "forecast"],
   "license": "MIT",
   "privacy_policies": ["https://futuresearch.ai/privacy/"]
 }