Algolia Recommend API
Browse all Algolia APIs★ Only Publicly Available OpenAPI DocumentAI/MLMl InferenceapiKey11 EndpointsREST
For Agents
Fetch product recommendations (related, frequently bought together, trending, looking similar) and manage merchandising rules for the Algolia Recommend models.
Use for: I need to fetch related products for a given SKU, Get frequently bought together items for a product, Find trending products in a category, Pin a specific product to the top of related recommendations
Not supported: Does not handle keyword search, event ingestion, or personalized search ranking - use the Algolia Search, Insights, and Personalization APIs for those. Use this API for product recommendations and merchandising rules only.
Jentic publishes the only available OpenAPI specification for the Algolia Recommend API, keeping it validated and agent-ready. The Recommend API serves product recommendations - related items, frequently bought together, trending items, looking similar - powered by models trained on Insights events. It also exposes a rules engine so merchandisers can pin, hide, or boost recommendations for specific objects. Eleven endpoints cover recommendations retrieval and rule management.
Install Jentic One Beta
Connect the Recommend API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Recommend API, 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://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | 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 Recommend API.
Retrieve related products, frequently bought together, trending, and looking-similar recommendations via POST /1/indexes/*/recommendations
Define merchandising rules to pin, hide, or boost specific objects in recommendations via POST /1/indexes/{indexName}/{model}/recommend/rules/batch
Search across recommend rules to audit current merchandising via POST /1/indexes/{indexName}/{model}/recommend/rules/search
Read or delete a single recommend rule by object ID via GET and DELETE /1/indexes/{indexName}/{model}/recommend/rules/{objectID}
Check the asynchronous task status after applying rule changes via GET /1/indexes/{indexName}/{model}/task/{taskID}
Use Cases
Patterns agents use Recommend API for, with concrete tasks.
★ Related-products carousel on a product detail page
Call /1/indexes/*/recommendations with model 'related-products', the indexName, and the current product's objectID to get a ranked list of similar items. The response is filtered through any active recommend rules, so merchandiser pins and hides apply without extra logic in the front-end.
POST /1/indexes/*/recommendations with requests=[{indexName:'products', model:'related-products', objectID:'sku_123', threshold:30}]
Frequently bought together for cart upsell
On the cart page, call the Recommend API with model 'bought-together' and the SKUs already in the cart to surface upsells trained on real co-purchase data. The model is updated automatically as new conversion events flow in via the Insights API, so recommendations stay fresh without retraining.
POST /1/indexes/*/recommendations with requests including model 'bought-together' for each cart objectID and aggregate the returned objectIDs
Merchandiser pin and hide rules
Define rules that pin a hero product to the top of related-products on a campaign page or hide an out-of-stock SKU across every model. Rule changes are asynchronous - poll /1/indexes/{indexName}/{model}/task/{taskID} until the rule has been indexed, then verify with a recommendations request.
POST /1/indexes/products/related-products/recommend/rules/batch with a rule pinning objectID 'sku_hero' to position 1 for all queries on the campaign category
Agent-driven recommendations in conversational commerce
An AI shopping assistant operating through Jentic can fetch related and bought-together items as part of a single conversation. The agent searches Jentic by intent, loads the getRecommendations schema, and executes - no manual integration with the Recommend endpoint.
Search Jentic for 'fetch Algolia related products', load getRecommendations, and execute for the SKU the user is looking at
Key Endpoints
11 endpoints — jentic publishes the only available openapi specification for the algolia recommend api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/1/indexes/*/recommendations
Fetch recommendations across one or more models
POST
/1/indexes/{indexName}/{model}/recommend/rules/batch
Create or update recommend rules in batch
POST
/1/indexes/{indexName}/{model}/recommend/rules/search
Search across recommend rules
GET
/1/indexes/{indexName}/{model}/recommend/rules/{objectID}
Get a single recommend rule
DELETE
/1/indexes/{indexName}/{model}/recommend/rules/{objectID}
Delete a recommend rule
GET
/1/indexes/{indexName}/{model}/task/{taskID}
Check rule indexing task status
POST
/1/indexes/*/recommendations
Fetch recommendations across one or more models
POST
/1/indexes/{indexName}/{model}/recommend/rules/batch
Create or update recommend rules in batch
POST
/1/indexes/{indexName}/{model}/recommend/rules/search
Search across recommend rules
GET
/1/indexes/{indexName}/{model}/recommend/rules/{objectID}
Get a single recommend rule
DELETE
/1/indexes/{indexName}/{model}/recommend/rules/{objectID}
Delete a recommend rule
GET
/1/indexes/{indexName}/{model}/task/{taskID}
Check rule indexing task status
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
x-algolia-application-id and x-algolia-api-key are stored encrypted in the Jentic vault. Agents receive scoped tokens at request time; raw keys never enter agent context.
Intent-based discovery
Agents search Jentic by intent (e.g. 'fetch related products' or 'add a recommend rule') and Jentic returns the matching Recommend operation with its input schema.
Time to first call
Direct integration: 1 day to wire recommendations retrieval, rule management, and task polling. Through Jentic: under 30 minutes - search, load schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Recommend API through Jentic.
Why is there no official OpenAPI spec for the Recommend API?
Algolia does not publish a standalone OpenAPI specification for the Recommend API. Jentic generates and maintains this spec so that AI agents and developers can call the Recommend API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does the Algolia Recommend API use?
Two API key headers: x-algolia-application-id and x-algolia-api-key with the appropriate ACL (search for read, editSettings for rule management). Through Jentic, both keys are stored in the vault and injected at request time.
Can I get frequently-bought-together recommendations with this API?
Yes. POST /1/indexes/*/recommendations with model 'bought-together' and one or more objectIDs. The response returns ranked suggestions trained on conversion events captured by the Insights API.
What are the rate limits for the Algolia Recommend API?
The spec does not declare explicit numeric rate limits. The Recommend API shares Algolia's standard search infrastructure, which scales per-cluster. Watch for 429 responses on burst traffic and back off using the Retry-After header.
How do I add a recommendation rule through Jentic?
Search Jentic for 'add Algolia recommend rule', load batchRecommendRules, and execute against the target index and model. Jentic returns the taskID; poll /1/indexes/{indexName}/{model}/task/{taskID} until indexed.
Do recommendations need Insights events to work?
Yes. Recommend models train on click and conversion events sent through the Insights API. Without enough event volume, models fall back to defaults or return empty for cold-start objectIDs.
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