Recommend API

Name: Recommend API API
Brand: Recommend API
Availability: InStock

★ 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.

Quickstart

Get started with Recommend API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"fetch related products from Algolia"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Recommend API 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}

GET STARTED

Start building with Recommend API API

Explore with Jentic

View OpenAPI Document

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 document for Recommend API, keeping it validated and agent-ready.

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.

Use Cases

Patterns agents use Recommend API 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

Why though 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.

Complementary

Algolia Insights API

Source of training events for Recommend models

Always pair with Insights - Recommend models do not learn without click and conversion events from Insights.

Complementary

Algolia Search API

Search powers product discovery; Recommend powers similar-item discovery

Use Search for query-driven discovery and Recommend for objectID-driven discovery on detail and cart pages.

Alternative

Algolia Personalization API

Different relevance lever - personalized search ranking rather than item recommendations

Choose Personalization when the goal is to reorder search results for the current user; choose Recommend when the goal is to surface related items on a detail page.

FAQs

Specific to using Recommend API 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.