Google Recommendations AI (Beta)
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For Agents
Manage product catalogs, ingest user events, and call serving endpoints to retrieve personalized recommendations from Google's Recommendations AI Beta service.
Use for: I need to retrieve product recommendations for a user viewing a product detail page, Import a fresh catalog of products from BigQuery into Recommendations AI, Ingest a batch of user events from yesterday into the recommendation model, Register a new prediction API key for our front-end app
Not supported: Does not handle full-text search, fraud scoring, or marketing campaign delivery - use for catalog management, user event ingestion, and recommendation serving only.
Recommendations AI (Beta) is Google Cloud's managed personalization service for predicting catalog items a user is likely to engage with next. The API exposes operations to manage catalogs and catalog items, ingest user events, register prediction API keys, and call serving endpoints that return ranked recommendations. Google strongly recommends new customers use the Retail API, which incorporates the GA version of these capabilities; this beta API remains for existing integrations.
Install Jentic One Beta
Connect the Recommendations AI (Beta) to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Recommendations AI (Beta), or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Frecommendations-ai-beta" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Frecommendations-ai-beta" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Recommendations AI (Beta) API.
Create and manage catalogs that hold the items eligible for recommendation
Import catalog items from Cloud Storage or BigQuery in bulk
Ingest user events (detail-page-view, add-to-cart, purchase) that train recommendation models
Register prediction API keys so client apps can call serving endpoints with restricted credentials
Call predict to retrieve a ranked list of recommended items for a user and event context
Collect user events through the dedicated collect endpoint for low-latency client-side ingestion
Use Cases
Patterns agents use Recommendations AI (Beta) API for, with concrete tasks.
★ Product Detail Page Recommendations
On a product detail page, the storefront calls predict with the current user, page event context, and the placement (e.g., others-you-may-like) to receive a ranked list of recommended catalog items. Predictions are personalized by the user-event history Recommendations AI has been trained on.
Call predict on placement projects/p/locations/global/catalogs/default_catalog/eventStores/default_event_store/placements/others_you_may_like with the user event payload for the current visitor.
Catalog and Event Backfill
Before recommendations are useful, the catalog must be imported and historical user events ingested. Recommendations AI provides import operations on catalogItems and userEvents that can pull from Cloud Storage or BigQuery, making it possible to backfill months of history in a single pipeline run.
Call POST /v1beta1/{+parent}/catalogItems:import with a Cloud Storage URI containing the catalog feed, then POST /v1beta1/{+parent}/userEvents:import for the historical event log.
Restricted Prediction Keys for Client Apps
Front-end apps should call predict with restricted, per-app API keys rather than full Cloud credentials. The API exposes predictionApiKeyRegistrations so each client app gets its own key tied to a single event store and catalog, reducing blast radius if a key leaks.
Register a prediction API key for our mobile app under the default event store, then ship the key to the app via remote config.
Agent-Driven Recommendation Operations
A merchandising agent uses Jentic to refresh catalog imports, ingest events, and call predict on demand to populate suggestion modules in marketing emails or off-site placements, without needing direct console access to the Recommendations AI project.
Search Jentic for predict product recommendations, load the schema for the placements predict operation, and execute it with the user and event context for a marketing email recipient.
Key Endpoints
17 endpoints — recommendations ai (beta) is google cloud's managed personalization service for predicting catalog items a user is likely to engage with next.
METHOD
PATH
DESCRIPTION
POST
/v1beta1/{+name}:predict
Retrieve ranked recommendations for a user event
POST
/v1beta1/{+parent}/catalogItems:import
Bulk import catalog items from GCS or BigQuery
POST
/v1beta1/{+parent}/userEvents:import
Bulk import historical user events
POST
/v1beta1/{+parent}/userEvents:collect
Collect a single user event in real time
GET
/v1beta1/{+parent}/catalogItems
List catalog items
GET
/v1beta1/{+parent}/predictionApiKeyRegistrations
List registered prediction API keys
POST
/v1beta1/{+name}:predict
Retrieve ranked recommendations for a user event
POST
/v1beta1/{+parent}/catalogItems:import
Bulk import catalog items from GCS or BigQuery
POST
/v1beta1/{+parent}/userEvents:import
Bulk import historical user events
POST
/v1beta1/{+parent}/userEvents:collect
Collect a single user event in real time
GET
/v1beta1/{+parent}/catalogItems
List catalog items
GET
/v1beta1/{+parent}/predictionApiKeyRegistrations
List registered prediction API keys
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Recommendations AI by hand means configuring Google OAuth 2.0 for control-plane calls, refreshing scoped access tokens, and tracking catalog and user-event resource names across the v1beta1 endpoints on recommendationengine.googleapis.com. Through Jentic you install once, import Recommendations AI from the API Directory, store the OAuth credential once, and your agent calls it.
Permission scoping
This API puts the catalog and event store in the URL path (/v1beta1/{+name} and /v1beta1/{+parent}/catalogItems), so a rule can pin your agent to one catalog: it can serve predictions and read catalog items there and nothing else. You choose the operations it may call, so ones like importing a catalog or ingesting user events are not included unless you add them.
Credential isolation
Your Google OAuth credential and prediction key are stored once, encrypted, by your own Jentic One instance and injected at execution time. Neither credential ever enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'predict recommendations' or 'import a catalog', and Jentic returns the matching Recommendations AI operation with its input schema so the agent calls the right endpoint without reading the v1beta1 discovery doc.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Recommendations AI (Beta) API through Jentic.
What authentication does the Recommendations AI Beta API use?
The API uses Google OAuth 2.0 with the cloud-platform scope for control-plane and import calls, and registered prediction API keys for client-side predict and collect calls. Through Jentic the OAuth credentials live in the vault and short-lived tokens are minted per call.
Should I use this API for new projects?
Google recommends new customers use the Retail API, which incorporates the GA version of these capabilities; this v1beta1 surface is maintained primarily for existing integrations. The migration path is documented in the Retail API docs.
What are the rate limits for Recommendations AI?
Predict QPS, import job, and event ingestion quotas are documented per project in the Recommendations AI quotas page. Import jobs are long-running operations and finish asynchronously, while predict calls are synchronous and sized for serving traffic.
How do I serve recommendations through Jentic?
Search Jentic for predict product recommendations, load the schema for the placements predict operation which maps to POST /v1beta1/{+name}:predict, and execute it with the userEvent payload and the placement resource name.
Is the Recommendations AI API free?
No, prediction calls and training data are billed according to the Recommendations AI pricing page. Catalog and event imports also incur Cloud Storage or BigQuery read charges depending on the source.
How do I ingest user events efficiently?
Use POST /v1beta1/{+parent}/userEvents:import for historical or bulk loads from Cloud Storage or BigQuery, and POST /v1beta1/{+parent}/userEvents:collect for low-latency single-event ingestion from browsers and apps.
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