---
title: "Getting Started"
id: "1794"
type: "page"
slug: "getting-started"
published_at: "2026-09-20T21:51:52+00:00"
modified_at: "2026-09-21T01:36:28+00:00"
url: "https://xedant.com/agents/data/docs/getting-started"
markdown_url: "https://xedant.com/agents/data/docs/getting-started.md"
excerpt: "Data Agent is an agent for collecting and preparing data. You describe in chat which…"
---

# Getting Started

[https://xedant.com/agents/data/docs/getting-started.md](https://xedant.com/agents/data/docs/getting-started.md)

Data Agent is an agent for collecting and preparing data. You describe in chat which data you need — the agent writes and maintains Python scripts on its own, runs them on demand or on a schedule, and puts the results into the Lakehouse store. Data Agent serves as the data source for [Analytics Agent](/agents/analytics)
: everything it collects and prepares, Analytics Agent shows in reports and charts. The ETL — extracting, transforming and loading data — is done entirely by agents: there is no manual data entry and no script editing in the interface.

## Who it is for

For business owners and teams who need regular data: competitor prices, order statistics, exports from external service APIs. Before, this meant hiring a developer or figuring out exports yourself. Here it is enough to explain to the agent what you need — it writes the code, sets the schedule and keeps the export working.

## System requirements

You only need a Linux server with Docker. The Python environment with all the data libraries is built into the image — nothing to install separately. The detailed walkthrough is on the [Data Agent Installation](/agents/data/install)
 page.

## First sign-in — self-registration

The login and password are not set in advance: on first launch the application is open for registration, and **the first account created becomes the administrator**. After that, registration closes. Sign-in works through a session token (90 days), passwords are stored as hashes — they cannot be read from the database.

## Interface sections

- **Home** — an overview: runs and harvests today, the instance’s state, quick links into every section;
- **Chats** — talking to the agent: this is where you ask for data and fixes in plain language. The section appears when `AGENT_API_URL` and `AGENT_API_KEY` are set (the address and key of your agent);
- **Scripts** — the tree of folders and files the agent wrote: every folder has a description, every file has a page with its code;
- **Runs** — the history of all runs with live output and logs;
- **Scheduler** — the run schedule: the agent manages it at your request; here you see what runs and when, with a “Run now” button;
- **Lakehouse** — the store of collected data, laid out by project and source: Parquet, databases, descriptions, logs;
- **Data Sources** — the registry of sources you can query: they appear automatically;
- **Harvesters** — data collectors: services for bulk internet search, page download and text processing by models;
- **Harvests** — bulk collection jobs: what the agent launched, how much is processed and what it cost;
- **Analytics** — an overview of the agent’s work: tokens, cost, load by hours and days;
- **Settings** — theme, language, font size, the Python interpreter for runs;
- **Mobile** — a mobile web app for iOS and Android: the same capabilities on your phone.

The dark theme is on by default; the interface language is English (each user picks their own in settings).

## Your first script in five minutes

1. Describe to the agent in chat which data you need and where it comes from (for example: “Every day, collect competitor prices from the catalog”).
2. The agent creates a source folder in the scripts folder: the export script `extract.py`, the database build script `load.py` and a description file.
3. Ask it to run — the data appears in the Lakehouse, and the source becomes available automatically.
4. Ask it to set a schedule — from then on the data refreshes on its own.
5. Connect the source in [Analytics Agent](/agents/analytics/docs) — and build reports and charts on fresh data.

## Further in the documentation

- [Chat with the Agent](/agents/data/docs/chat) — how to ask for tasks, models and the message queue;
- [Scripts](/agents/data/docs/scripts) — how the scripts folder is arranged and what lives in it;
- [Runs](/agents/data/docs/runs) — running scripts manually and on a schedule;
- [Scheduler](/agents/data/docs/scheduler) — the run schedule and its rules;
- [Lakehouse](/agents/data/docs/lakehouse) — the data store: what lies where;
- [Data Sources](/agents/data/docs/sources) — how data becomes ready for queries;
- [Harvesters](/agents/data/docs/harvesters) — bulk data collection: search, page download and text processing by models;
- [Data for Analytics Agent](/agents/data/docs/for-analytics) — the collect → prepare → present pipeline;
- [Agent Analytics](/agents/data/docs/analytics) — tokens, cost, load;
- [Xedant Agent Integration](/agents/data/docs/agent) — how the pairing with the central agent works.

[← Back to the documentation index](/agents/data/docs)