Algolia Recommend API

Name: Recommend API
Brand: Recommend API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl InferenceapiKey11 EndpointsREST

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.

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 | sh

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 instance

Jentic 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

GET STARTED

Start building with Recommend API

Explore with Jentic

View OpenAPI Document

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.

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

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Your Algolia API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'get related product recommendations' or 'search merchandising rules', and Jentic returns the matching Algolia Recommend operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

Time to first call

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 with Jentic One, the self-hosted execution layer.

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.

Can I limit what my agent is allowed to do with the Algolia Recommend API?

Yes. Because you self-host Jentic One, your own rules decide which Recommend operations your agent may call and which Algolia credentials it uses. Since the index, model, and rule IDs sit in the URL path (for example /1/indexes/{indexName}/{model}/recommend/rules/{objectID}), you can pin the agent to a single index and model so it only fetches recommendations there. You choose the allowed operations, so an action like deleting a merchandising rule stays off the table unless you explicitly grant it.

Algolia Recommend API

Connect the Recommend API to your agent

Capabilities

Start building with Recommend API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs