Build Your First AI Data Analyst Chatbot
VerifiedBuild an AI chatbot for data analysis using OpenAI and tools.
What this workflow does
This workflow deploys an AI Agent that processes queries via OpenAI, maintains conversation memory, and executes calculations or sub-workflows through dedicated tools.
It suits data analysts, developers, and automation users who need AI-driven query handling and computation in n8n.
Who is this for?
Data analysts, researchers, developers, and automation enthusiasts who need to query datasets and run calculations through an AI interface.
What problem it solves
Manually retrieving data from sources and performing analysis is slow and repetitive; this workflow gives an AI agent direct access to tools so it can fetch data and compute results on demand.
Live workflow preview
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Open the template on n8n to import and run it. View source template →
What it automates
Sales data queries
Ask the chatbot for monthly revenue totals and let the Calculator tool compute sums from retrieved rows.
Research metric checks
Pull specific dataset slices via the AI Agent and verify statistical values without writing SQL each time.
Quick KPI reports
Use the chatbot to fetch latest figures and run basic math operations for ad-hoc business reviews.
How the workflow works
The 7 nodes in this automation, in order.
- 1HTTP RequesthttpRequest
- 2Codecode
- 3AI Agent@n8n/n8n-nodes-langchain.agent
- 4OpenAI Chat Model@n8n/n8n-nodes-langchain.lmChatOpenAi
- 5Simple Memory@n8n/n8n-nodes-langchain.memoryBufferWindow
- 6Calculator@n8n/n8n-nodes-langchain.toolCalculator
- 7Call n8n Workflow Tool@n8n/n8n-nodes-langchain.toolWorkflow
Apps & integrations used
How to set up Build Your First AI Data Analyst Chatbot
- 1Import the workflow JSON into your n8n instance.
- 2Add credentials for the OpenAI Chat Model node.
- 3Configure the Simple Memory node to store conversation context.
- 4Connect the Calculator tool to the AI Agent.
- 5Set up the Call n8n Workflow Tool if you want to trigger sub-workflows.
- 6Activate the workflow and test via the chat trigger or webhook.
How to customize this workflow
- →Replace OpenAI Chat Model with another supported LLM.
- →Swap HTTP Request calls for direct database nodes like Postgres.
- →Add more tools to the AI Agent for additional data sources.
- →Change the trigger from chat to a scheduled or webhook node.
Build Your First AI Data Analyst Chatbot: pros & cons
Pros
- +Combines data retrieval and math in one conversational interface
- +Uses built-in n8n AI Agent and memory nodes for quick setup
- +Easy to extend with additional tools
- +Intermediate template that demonstrates practical agent patterns
Cons
- –Requires valid OpenAI API credits to run
- –No native Google Sheets node listed so external calls must be built
- –Simple Memory may lose context on long sessions
Frequently asked questions
It creates an AI chatbot that can pull data via tools and run calculations using an AI Agent connected to OpenAI and Calculator nodes.
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