Skip to main content
Data Tables give each workspace structured, SQL-backed storage — rows and typed columns you manage like a spreadsheet, but that your flows and agents can read, write, and query with SQL. Use them to stage reference data, capture flow output, or give an agent a source of truth to look things up in.

​
Concepts

  • Table — a named collection of rows with a fixed set of columns. Every table has an implicit id primary key.
  • Column types — string, number, boolean, datetime, and file.
  • Rows — records keyed by id. The id is a UUID by default, or an auto-incrementing integer if you choose the serial id type at creation.
  • Workspace-scoped — tables belong to a workspace; access follows your workspace role.
Alongside your own tables, the platform exposes read-only platform views (workflows, runs, users, agents, conversations, knowledge bases) you can query the same way.

​
Working with tables

In the app

Create tables, add columns, and edit rows in a spreadsheet-style UI. Import from CSV to bootstrap a table, and export back to CSV.

In flows

Read from and write to tables inside a flow, and run SQL with the query node — e.g. look up a record mid-flow or persist results.

From agents

Give an agent the Data Tables or SQL tool so it can answer questions from your data with read-only queries. See Agent Tools.

Programmatically

Full CRUD, bulk insert, CSV import/export, and SQL via the SDK and REST API.

​
SQL queries

Query across the workspace’s tables with read-only SQL. Queries are guarded server-side (read-only, scoped to your workspace’s data) and reference a table by its sql_name (the lowercased table name):

​
Next steps

Tables in the SDK

Create tables, manage columns and rows, run SQL, import/export CSV.

Tables API

The full REST reference for the tables endpoints.