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Run LLM research agents at scale. Use them to intelligently sort, filter, merge, dedupe, or add columns to pandas dataframes. See the [docs site](https://everyrow.io/docs). Scales to tens of thousands of LLM agents on tens of thousands of rows.
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Run LLM research agents at scale. Use them to intelligently sort, filter, merge, dedupe, or add columns to pandas dataframes. Scales to tens of thousands of LLM agents on tens of thousands of rows, all from a single python method. See the [docs site](https://everyrow.io/docs).
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```bash
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pip install everyrow
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```
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The best experience is inside Claude Code.
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```bash
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claude plugin marketplace add futuresearch/everyrow-sdk
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claude plugin install everyrow@futuresearch
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```
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[Get] an API key at [everyrow.io/api-key](https://everyrow.io/api-key) ($20 free credit), then:
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Get an API key at [everyrow.io/api-key](https://everyrow.io/api-key) ($20 free credit), then:
Intelligent data processing can handle tens of thousands of LLM calls, or thousands of LLM web research agents, in each single operation.
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Deduplicate when fuzzy matching falls short.
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| Operation | Intelligence | Scales To |
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|---|---|---|
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|[**Screen**](https://everyrow.io/docs/reference/SCREEN)| Filter by criteria that need judgment | 10k rows |
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|[**Rank**](https://everyrow.io/docs/reference/RANK)| Score rows from research | 10k rows |
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|[**Dedupe**](https://everyrow.io/docs/reference/DEDUPE)| Deduplicate when fuzzy matching fails | 20k rows |
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|[**Merge**](https://everyrow.io/docs/reference/MERGE)| Join tables when keys don't match | 5k rows |
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|[**Research**](https://everyrow.io/docs/reference/RESEARCH)| Web research on every row | 10k rows |
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```python
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from everyrow.ops import dedupe
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result =await dedupe(
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input=contacts,
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equivalence_relation="""
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Two rows are duplicates if they represent the same person.
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Account for name abbreviations, typos, and career changes.
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""",
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)
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print(result.data.head())
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```
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"A. Butoi" and "Alexandra Butoi" are the same person. "AUTON Lab (Former)" indicates a career change, not a different org. Results include `equivalence_class_id`, `equivalence_class_name`, and `selected` (the canonical record).
Join two tables when the keys don't match exactly. Or at all.
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```python
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from everyrow.ops import merge
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result =await merge(
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task="Match each software product to its parent company",
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left_table=software_products,
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right_table=approved_suppliers,
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merge_on_left="software_name",
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merge_on_right="company_name",
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)
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print(result.data.head())
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```
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Knows that Photoshop belongs to Adobe and Genentech is a Roche subsidiary, even with zero string similarity. Fuzzy matching thresholds always fail somewhere: 0.9 misses "Colfi" ↔ "Dr. Ioana Colfescu", 0.7 false-positives on "John Smith" ↔ "Jane Smith".
See the full [API reference](https://everyrow.io/docs/api), [guides](https://everyrow.io/docs/guides), and [notebooks](https://everyrow.io/docs/notebooks), (for example, see our [notebook](https://everyrow.io/docs/notebooks/llm-web-research-agents-at-scale) running a `Research` task on 10k rows, running agents that used 120k LLM calls.)
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---
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## Agent Tasks
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## Web Agents
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Web research on single inputs or entire dataframes. Agents are tuned on [Deep Research Bench](https://arxiv.org/abs/2506.06287), our benchmark for questions that need extensive searching and cross-referencing.
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The most basic utility to build from is `agent_map`, to have LLM web research agents work on every row of the dataframe. Agents are tuned on [Deep Research Bench](https://arxiv.org/abs/2506.06287), our benchmark for questions that need extensive searching and cross-referencing, and tuned to get correct answers at minimal cost.
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```python
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from everyrow.ops import single_agent, agent_map
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classCompanyInput(BaseModel):
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company: str
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# Single input
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# Single input, run one web research agent
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result =await single_agent(
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task="Find this company's latest funding round and lead investors",
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input=CompanyInput(company="Anthropic"),
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)
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print(result.data.head())
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#Batch
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#Map input, run a set of web research agents in parallel
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result =await agent_map(
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task="Find this company's latest funding round and lead investors",
Add computed columns using [`pandas.DataFrame.eval`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.eval.html#pandas.DataFrame.eval), no AI agents needed.
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See the API [docs](https://everyrow.io/docs/reference/RESEARCH.md), a case study of [labeling data](https://everyrow.io/docs/classify-dataframe-rows-llm) or a notebook for [researching government data](https://everyrow.io/docs/notebooks/research-and-rank-permit-times) at scale.
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```python
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from everyrow.ops import derive
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result =await derive(
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input=orders_dataframe,
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expressions={"total": "price * quantity"},
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)
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print(result.data.head())
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```
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## Sessions
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`derive` is useful for adding simple calculated fields before or after other operations. It's much faster and cheaper than using AI agents to do the computation.
Sessions are created automatically for one-off operations. For multiple operations, use an explicit session:
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You can also use a session to output a URL to see the research and data processing in the [everyrow.io/app](https://everyrow.io/app) application, which streams the research and makes charts. Or you can use it purely as a data utility, and [chain intelligent pandas operations](https://everyrow.io/docs/chaining-operations) with normal pandas operations.
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```python
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from everyrow import create_session
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asyncwith create_session(name="My Session") as session:
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print(f"View session at: {session.get_url()}")
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# All operations here share the same session
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```
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Sessions show up on the [everyrow.io](https://everyrow.io) dashboard.
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### Async operations
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All ops have async variants for background processing:
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## Install
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See the [installation docs](https://everyrow.io/docs/).
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