Algolia Recommend API
Browse all Algolia APIs63
AI ReadinessAI-Aware (B)63/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey11 EndpointsREST
Know of an official OpenAPI document? Contribute it →
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://jentic.com/install.sh?src=apis&api=%2Fapis%2Falgolia.com%2Falgolia-recommend-api" | 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%2Falgolia.com%2Falgolia-recommend-api" | 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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
R
Recommend API
- AI-Aware (B)63/100
48
Foundational Compliance67
Developer Experience & Jentic Compatibility58
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
48
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: D+Signals: 4
88%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
2%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
67
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: BSignals: 4
19%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
48%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
58
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: C+Signals: 4
69%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
61%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Algolia Recommend API by hand means sending its API key, resolving the {appId}.algolia.net host, and managing recommendation queries and merchandising rules yourself. Through Jentic you install once, import the Recommend API from the API Directory, store the API key once, and your agent calls it.
Permission scoping
The Recommend API puts the index, model, and rule ids in the URL path (/1/indexes/{indexName}/{model}/recommend/rules/{objectID}, /1/indexes/{indexName}/{model}/task/{taskID}), so a rule can pin your agent to one index and model. You choose the operations it may call, so deleting a merchandising rule is not included unless you add it.
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.
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 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.
GET STARTED