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Documentation Index

Fetch the complete documentation index at: https://docs.botbrains.io/llms.txt

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

botBrains lets you export conversations, messages, and user data for compliance, custom analytics, or backup. All exports respect your project permissions.

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Export Methods

Full Dataset ExportEntity-Specific Export
WhatComplete snapshot of all project dataFiltered download of one entity type (Conversations, Messages, or Users)
FormatJSONL (Users → Conversations → Messages hierarchy)CSV, JSONL, Excel, or Markdown
HowSettings → Data Export, processed in the background, download link sent by emailExport button on any Conversations, Messages, or Users list page, downloads immediately
Limits2 per day per project; download link valid for 7 days10,000 rows per export
Use whenFull backups, GDPR requests, AI analysis across all dataFiltered subsets, spreadsheet analysis, sharing with stakeholders
Full Dataset Export settings dialog showing export options in the Data Export settings page

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Export Formats

FormatBest for
CSVExcel, Google Sheets, quick manual analysis
JSONLProgrammatic processing, data pipelines, preserving nested data (Users → Conversations → Messages)
Excel (CSV)Microsoft Excel with correct UTF-8 encoding and date handling
MarkdownCopying conversations into an LLM (ChatGPT, Claude) for ad-hoc analysis
Entity-specific export dialog on the Conversations list page showing format and filter options
If CSV displays special characters incorrectly in Excel, use the Excel (CSV) format or import via Excel’s “Get Data” feature with UTF-8 encoding.

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Media Files in Exports

Exports include time-limited download URLs for media attachments (images, files), not the files themselves. These URLs expire after 7 days. Download media files after exporting if you need permanent copies.

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Custom Analysis with AI Tools

The Full Dataset Export (JSONL) works well with AI tools like ChatGPT or Claude. Upload the file and use this prompt template: 
You are a data analyst, using your Code Interpreter / Python Interpreter tools
to write code and answer my questions. You've been given a JSON Lines file, of
the conversations had by the ai-assistant, operators (human agents) and
customers. Each line is a user profile, with all their conversations and all
messages within those conversations, including metadata. For legacy reasons, not
all conversations have a user, so a mock user with
ID 00000000-0000-0000-0000-000000000000 is assigned all other conversations.

Follow this workflow:
1. Familiarize yourself with the data model by looking at the first 5 lines.
2. Think about my question and the appropriate chart / way to answer my question
   with the given values.
3. Work to write the code and find appropriate outputs format (chart-type,
   tables, singular values...) for the data.

Question: [Your question here]
Example questions:
  • “Which users send the most messages? Show a table sorted by message count.”
  • “What common issues do users with external_attributes.plan = PREMIUM describe?”
  • “Find conversations rated 1–2 and categorize the top complaint themes.”
For best results, ask the AI to first filter by your criteria (poor ratings, specific attributes), then analyze the message content to identify patterns.
You are responsible for ensuring exported data complies with your organization’s data policies (GDPR, CCPA, etc.) before uploading to external AI tools.

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Next Steps

Conversations

Filter and review conversations before exporting

Metrics

Use built-in analytics before resorting to custom exports