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Export AI Agent Conversation Logs from Postgres to Google Sheets

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Agent Studio

Last update

Last update 8 days ago

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Overview

This n8n workflow retrieves AI agent chat memory logs stored in Postgres and pushes them to Google Sheets, creating one sheet per session. It’s useful for teams building chat-based products or agents and needing to review or analyze session logs in a collaborative format.

Who is it for

  • Anyone with an AI Agent in Production storing the conversation logs in Postgres (or Supabase) who wants to see transcript and have control
  • Product teams building AI agents or assistants.
  • Teams that want to centralize conversation history for analysis or support.
  • Anyone managing AI chat memory and needing to explore it in a spreadsheet.

Prerequisites

  • A Postgres database with a n8n_chat_histories table with an AI Agent connected to it. If you need an example, you can follow this tutorial
  • Once done, you need to run the Postgresql query to add the created_at column (see Setup > Add a datetime column)
  • Google Sheets access and OAuth credentials connected to n8n.
  • A Google Sheets document set up as a template (see below).

Google Sheets Template

This workflow expects a Google Sheets file where each session will be stored in its own tab.
A basic tab layout is duplicated and renamed with the session ID.
👉 Use this template as a starting point
Note: You can hide the template after the first tabs have been created

How it works

  1. Trigger
    The workflow can be launched manually or on a schedule (e.g. daily at noon).

  2. Retrieve sessions
    Runs a SQL query to get distinct session_id values from the n8n_chat_histories table.

  3. Loop over sessions
    For each session:

    • Clears the corresponding sheet (if it exists).
    • Duplicates the template tab.
    • Renames it with the current session_id.
  4. Fetch messages
    Selects all messages linked to the session from Postgres.

  5. Append to sheet
    Adds each message to the Google Sheet with columns:

    • Who: speaker role (user, assistant, etc.)
    • Message: text content
    • Date: timestamp from created_at, formatted yyyy-MM-dd hh:mm:ss

Notes

  • The sheet is cleared and rebuilt each run to ensure logs are up-to-date.
  • If a sheet for a session doesn’t exist, it will be created by duplicating the first tab (template)
  • You can group sessions under a persistent ID (like user_id) by overriding session_id in your memory config.
  • Works perfectly with Supabase by using PG credentials from the connection pooler.

👉 If you're looking for a solution to better visualize and analyse conversations, reach out to us!