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Track OpenAI token usage and AI agent metrics with Google Sheets dashboard

HunyaoHunyao
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2/3/2026
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What it does Captures token usage and cost from your AI Agent/LLM. Logs model, tokens, cost, tool use, and conversation I/O to Google Sheets for simple observability and billing.

Perfect for Developers adding usage monitoring to AI agents. Teams needing cost transparency in prototypes.

How it works

  1. Chat Trigger collects user input for the AI Agent.
  2. A Set node injects metadata like workflow, execution, and client IDs.
  3. LangChain Code node returns a configured Chat model with a callback that reads usage metadata.
  4. The callback computes input, output, and total costs based on per‑million token prices you define.
  5. It appends token metrics to a Google Sheet via the Google Sheets Tool.
  6. The Agent records intermediate tool calls.
  7. An If node checks whether a tool was used.
  8. When tools are used, the workflow logs input, output, tool name, and metadata to an Observability sheet.

How to use SELF-HOSTED N8N ONLY - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud.

Requirements Self-hosted version of n8n

If you have any questions in running the workflow, see the attached video: https://youtu.be/JSulRS128MA

n8n Workflow: OpenAI Token Usage and AI Agent Metrics with Google Sheets Dashboard

This n8n workflow is designed to track and log metrics related to AI agent interactions, specifically focusing on OpenAI token usage and other relevant data, into a Google Sheet. It provides a foundation for building a dashboard to monitor AI agent performance and cost.

What it does

This workflow processes incoming chat messages, potentially from an AI agent, and logs key metrics to a Google Sheet.

  1. Listens for Chat Messages: The workflow is triggered by an incoming chat message.
  2. Processes AI Agent Output: It includes an "AI Agent" node, suggesting it's designed to interact with or process output from an AI agent.
  3. Transforms Data: A "LangChain Code" node and an "Edit Fields (Set)" node are used to transform and prepare the data for logging. This likely involves extracting specific metrics like token usage, agent actions, and responses.
  4. Conditional Logic: An "If" node allows for conditional processing, potentially to filter messages or apply different logic based on certain criteria.
  5. Logs to Google Sheets: The processed data is then appended as a new row to a specified Google Sheet, enabling persistent storage and dashboard creation.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Account: A Google account with access to Google Sheets.
  • Google Sheets Credential: An n8n credential for Google Sheets (OAuth2 recommended).
  • AI Agent/LangChain Setup: Depending on your specific AI agent integration, you might need an OpenAI API key or other credentials for the "AI Agent" and "LangChain Code" nodes.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets credential.
    • If using an AI agent that requires authentication (e.g., OpenAI), configure the necessary credentials for the "AI Agent" node.
  3. Configure Google Sheet Node:
    • Specify the Spreadsheet ID and Sheet Name where you want to log the data. Ensure the sheet has appropriate headers matching the data you intend to log (e.g., Timestamp, User Input, Agent Response, Tokens Used, Agent Actions).
  4. Configure AI Agent Node:
    • Adjust the "AI Agent" node settings according to your AI agent's configuration (e.g., model, prompts, tools).
  5. Configure LangChain Code and Edit Fields Nodes:
    • Review and modify the "LangChain Code" and "Edit Fields (Set)" nodes to extract and format the specific data points you wish to log from your AI agent's output. This is where you'll define how to parse token usage, agent steps, etc.
  6. Activate the Workflow: Once configured, activate the workflow. It will start listening for chat messages and logging data to your Google Sheet.

This workflow provides a robust framework for monitoring your AI agent's interactions and resource consumption, laying the groundwork for insightful analytics and cost management.

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