---
title: "FAQ"
id: "1792"
type: "page"
slug: "faq"
published_at: "2026-09-20T21:51:48+00:00"
modified_at: "2026-09-21T01:36:28+00:00"
url: "https://xedant.com/agents/data/faq"
markdown_url: "https://xedant.com/agents/data/faq.md"
excerpt: "The agent. You describe in chat which data you need — it creates and maintains…"
---

# FAQ

[https://xedant.com/agents/data/faq.md](https://xedant.com/agents/data/faq.md)

## Who writes the scripts?

The agent. You describe in chat which data you need — it creates and maintains the scripts: writes new ones, fixes errors, adds sources. There is no code editor in the interface: all script work happens through a conversation with the agent, while the interface is there for running, scheduling and viewing results.

## How is Data Agent different from manual data exports?

A manual export is one-off effort every time: someone has to write or fix code, remember to run it, watch for errors. Data Agent does all of it on its own: the scripts are already written, they run on demand or on a schedule, the history and logs of every run are kept, and the collected data is immediately ready for visualization in Analytics Agent.

## How is Data Agent related to Analytics Agent?

Data Agent is the data supplier for Analytics Agent. Collection and preparation happen here: scripts, runs, the schedule, the store. Visualization happens in Analytics Agent: reports, charts and dashboards built on the databases Data Agent prepared. Together they form one pipeline: collect → prepare → present.

Analytics Agent gets read-only access to the data — databases can be viewed but not damaged. And when a report needs to be added, fixed or extended, Analytics Agent hands the work to Data Agent through its protected API (key access, every call verified): it puts or replaces a script, runs it, refreshes the data, and sets a schedule when needed. Only Data Agent changes the data — the store stays in order.

## Where is the data stored?

In the Lakehouse — the `/lakehouse` folder on your server, organized by project and source. Each source folder holds: the raw Parquet files, a SQLite database for visualization, a README description and run logs. The data is yours: move it by simply copying the folder.

## How is the schedule set up?

The schedule is one file, `schedule.yml`, in the scripts folder. The agent changes it at your request (“run every day at 8 a.m.”). Changes are picked up on the fly, without a restart, and runs missed to downtime never pile up — the schedule resumes cleanly, and a catch-up option recovers missed runs.

## Do I need to know how to program?

No. Scripts, schedules and sources — the agent creates all of it from a verbal description. Python is only needed by the agent, and its environment is already built into the Docker image.

## What do I need to run it?

A Docker host: one container, a scripts folder, a persistent data folder and a Lakehouse root. Chat with the agent is enabled with the address and API key of Xedant Agent.

## Can I use it without Xedant Agent?

Yes: runs, the scheduler, the Lakehouse, data sources and browsing results all work on their own. The integration adds the agent chat — and the agent is exactly what writes and maintains the scripts, so without a connection to Xedant Agent there is no one to collect new data. Already collected data can be viewed and visualized in Analytics Agent.

## How do I collect data from hundreds of pages?

Bulk collection is the job of harvesters: services for internet search, page download and text processing by models. You configure them once in the Harvesters section, and the agent launches them at your request — for example, “compare competitor prices across three hundred pages”. The result is visible in the Harvests section: how much is processed, how much was spent and how long is left. Whether paid keys are needed depends on the service: DuckDuckGo search works without a key, while search engines like Serper and AI models need a key and cost money. The cost is known in advance: every harvester states its price per query, per download or per token, and the statistics show what has been spent. Details in the [Harvesters](/agents/data/docs/harvesters)
 article.