Catalog-Grounded Ecommerce Product Recommendation Agent
Let an Agent search your live product DataTable and recommend only in-stock SKUs that satisfy the shopper budget and exclusions.
The full canvas, before you import it
Click any node to see its config.
Click a node to select it — same as the Heym editor; the panel shows its settings.
4 nodes · Free & source-available
Catalog-Grounded Ecommerce Product Recommendation Agent
Build useful product recommendations without letting the model invent catalog items, prices, or availability. The Product Advisor receives a shopper request and calls a Heym DataTable node as a canvas tool. It can reason across the returned rows, but every recommendation must trace back to an in-stock catalog record.
What this workflow does
- ShopperRequest captures the need, maximum budget, preferred category, and excluded materials
- SearchProductCatalog is exposed to the Agent as a constrained catalog-search tool
- ProductAdvisor must search the catalog and reject rows that are out of stock, over budget, or excluded
- RecommendationResponse returns stable recommendation JSON for a storefront, chat portal, or API
Use cases
- Ecommerce shopping assistants
- Product comparison widgets
- Guided selling for large catalogs
- Grounded recommendations in customer support chat
Setup
Create a Product Catalog DataTable with SKU, title, category, price, stock status, materials, product URL, and short description columns. Select it in SearchProductCatalog and connect an LLM credential to ProductAdvisor. Keep the tool limited to read operations and test exclusion and out-of-stock cases before publishing.
How to import this template
- 1Click Import → Copy JSON on this page.
- 2Open your Heym and navigate to a workflow canvas.
- 3PressCmd+V/Ctrl+V— nodes appear instantly.
- 4Add your API keys in the node config panels and click Run.
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