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Documentation Index

Fetch the complete documentation index at: https://docs.databar.ai/llms.txt

Use this file to discover all available pages before exploring further.

Databar is a platform that brings the simplicity of a spreadsheet to the world of APIs and structured data. It connects to hundreds of data providers, web scrapers, and third-party services out of the box, letting you build powerful data workflows by pointing and clicking rather than writing code. Pick a data point, click run, and watch your table fill itself.
Databar platform overview showing a table with enriched data

Core capabilities

Tables

Store and manage structured data with columns, filters, and cell-level enrichment statuses.

Enrichments

Combine your existing data with third-party providers to fill in the gaps automatically.

API Network

Access 100+ data providers without managing API keys — pay per use with credits.

Developer tools

REST API, Python SDK, CLI, and MCP server for programmatic access.

How it works

Databar combines several building blocks into a single workflow:
  • Enrichments — attach data providers to your table columns. When you run an enrichment, each row is sent to the provider and the results are written back into your table.
  • Waterfalls — chain multiple providers together with automatic fallback. If the first provider returns no data, the next one picks up.
  • Connectors — bring your own API keys for services you already pay for, or add entirely custom endpoints.
  • Exporters — push enriched data to HubSpot, Salesforce, Google Sheets, webhooks, or other tables.
  • Formulas — transform data in-place with Excel formulas, JQ expressions, merge columns, deduplication, and Table Lookup (VLOOKUP across tables).

AI features

  • AI Researcher — an autonomous agent that finds and compiles information across the web for each row in your table.
  • AI Prompt Templates — run custom prompts against your data using LLMs, with configurable templates and variables.

Extensions and integrations

  • Chrome Extension — scrape structured data from any website directly into a Databar table.
  • Google Sheets Extension — run Databar enrichments without leaving your spreadsheet.
  • n8n Integration — connect Databar to n8n workflows for complex automation scenarios.
  • Webhooks — receive data from external systems instantly, with zero-config webhook URLs per table.

Who is Databar for?

Databar is built for teams that need structured data but don’t want to maintain pipelines:
  • Sales and RevOps — enrich leads, verify contact info, score accounts
  • Marketing — build prospect lists, research competitors, monitor mentions
  • Recruiting and HR — source candidates, verify profiles, track outreach
  • E-commerce — monitor pricing, aggregate product data, track suppliers
  • Anyone with a data workflow — the platform is flexible enough to handle any scenario where you need to collect, enrich, or transform structured data

Recent additions

Databar ships updates frequently. Recent highlights include:
  • Table Lookup for cross-referencing data between tables
  • Merge Columns and JQ formulas for in-table transformations
  • Cell-level enrichment statuses showing exactly what happened at each step
  • Instant webhook setup for receiving external data
  • 9+ new importers for pulling data from additional sources
  • Folders for organizing tables by project or team
  • Infinite scroll for working with large datasets

Get started

Tables

Create your first table

REST API

Quickstart for the REST API

Python SDK

Install the Python SDK

MCP Server

Connect via MCP

Enrichments

Learn about enrichments

Credits

Understand pricing