Merge

merge left-joins two DataFrames using LLM-powered agents to resolve the key mapping instead of requiring exact or fuzzy key matches. Agents resolve semantic relationships by reasoning over the data and, when needed, searching the web for external information to establish matches: subsidiaries, regional names, abbreviations, and product-to-parent-company mappings.

Examples

from everyrow.ops import merge

result = await merge(
    task="Match each software product to its parent company",
    left_table=software_products,   # table being enriched — all rows kept
    right_table=approved_vendors,    # lookup/reference table — columns appended
    # merge_on_left/merge_on_right omitted: auto-detection handles most cases.
    # Only specify them when you expect exact string matches on specific columns
    # or want to draw agent attention to them.
)
print(result.data.head())

For ambiguous cases, add context. Here merge_on_left/merge_on_right are set because the column names ("sponsor", "company") are too generic for auto-detection:

result = await merge(
    task="""
        Match clinical trial sponsors to parent pharma companies.

        Watch for:
        - Subsidiaries (Genentech → Roche, Janssen → J&J)
        - Regional names (MSD is Merck outside the US)
        - Abbreviations (BMS → Bristol-Myers Squibb)
    """,
    left_table=trials,              # table being enriched — all rows kept
    right_table=pharma_companies,   # lookup table
    merge_on_left="sponsor",        # specified: draws agent attention to this column
    merge_on_right="company",       # specified: draws agent attention to this column
)
print(result.data.head())

What you get back

A DataFrame with all left table columns plus matched right table columns. Rows that don't match get nulls for the right columns (like a left join).

Parameters

Name	Type	Description
`task`	str	How to match the tables
`left_table`	DataFrame	The table being enriched — all its rows are kept in the output (LEFT JOIN).
`right_table`	DataFrame	The lookup/reference table — its columns are appended to matches; unmatched left rows get nulls.
`merge_on_left`	Optional[str]	Only set if you expect exact string matches on this column or want to draw agent attention to it. Auto-detected if omitted.
`merge_on_right`	Optional[str]	Only set if you expect exact string matches on this column or want to draw agent attention to it. Auto-detected if omitted.
`relationship_type`	Optional[str]	`"many_to_one"` (default) — multiple left rows can match one right row. `"one_to_one"` — unique matching between left and right rows. `"one_to_many"` — one left row can match multiple right rows. `"many_to_many"` — multiple left rows can match multiple right rows. For `one_to_many` and `many_to_many`, multiple matches are joined with `" \| "` in each added column.
`use_web_search`	Optional[str]	`"auto"` (default), `"yes"`, or `"no"`. Controls whether agents use web search to resolve matches.
`session`	Session	Optional, auto-created if omitted

Performance

Size	Time	Cost
100 × 50	~3 min	~$2
2,000 × 50	~8 min	~$9
1,000 × 1,000	~12 min	~$15

Via MCP

MCP tool: everyrow_merge

Parameter	Type	Description
`left_csv_path`	string	Path to the table being enriched (left join)
`right_csv_path`	string	Path to the lookup/reference table
`task`	string	How to match rows across tables

Related docs

Guides

Fuzzy Join Without Matching Keys

Case Studies

Blog posts