IBM Research AI for Fashion API

Name: IBM Research AI for Fashion API API
Brand: IBM Research AI for Fashion API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLVisionapiKey15 EndpointsREST

For Agents

Index fashion product catalogs and run visual or text-based product search to find similar items from a customer image or query.

Connect the IBM Research AI for Fashion API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the IBM Research AI for Fashion 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.

Install Jentic One Beta (Self-Hosted)

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

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 IBM Research AI for Fashion API API.

Create and delete fashion product catalogs scoped per tenant

Index individual products with images, attributes, and metadata for search

Run visual search against an uploaded image to retrieve visually similar fashion items

Run text search across catalog products using natural-language fashion vocabulary

GET STARTED

Start building with IBM Research AI for Fashion API API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for IBM Research AI for Fashion API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for IBM Research AI for Fashion API, keeping it validated and agent-ready. The API provides catalog management, visual search, visual browse, and text search tailored to fashion product datasets. Agents can index a product catalog, query it by image or natural-language description, and retrieve visually similar products from a customer-provided image. It is designed for retailers, marketplaces, and shopping assistants that need product discovery grounded in real apparel imagery.

Use Cases

Patterns agents use IBM Research AI for Fashion API API for, with concrete tasks.

★ Visual Product Discovery for Shoppers

Let shoppers upload an inspiration photo and surface visually similar items from a retailer's catalog. The API ranks catalog products by visual similarity using deep learning models trained on fashion imagery, returning matching product ids and metadata. Integration takes a few hours once a catalog has been indexed via the visual_search_index endpoint.

POST a customer photo to /catalog/{catalog_name}/visual_search and return the top five matching product ids with their stored attributes

Catalog Management for Fashion Retailers

Maintain a structured catalog of fashion products with consistent attributes, images, and identifiers. Use the catalog and product endpoints to add, update, or remove SKUs, then trigger reindexing so visual and text search stay current. Suitable for mid-size retailers managing thousands of products who need search backed by fashion-specific models.

Create a catalog named summer-2026, add ten products via /catalog/{catalog_name}/products/{id}, then call POST /catalog/{catalog_name}/visual_search_index to build the index

Natural-Language Fashion Search

Let customers describe what they are looking for in plain language and return matching products. The text search endpoint understands fashion vocabulary such as silhouette, neckline, and pattern terms, returning ranked product results. Useful for chat-based shopping assistants and on-site search bars.

Call GET /catalog/{catalog_name}/text_search with the query v-neck red maxi dress and return the top product matches

AI Shopping Agent via Jentic

Build an AI agent that handles fashion shopping queries end to end by combining visual and text search through Jentic. Jentic resolves the right operation from an intent like find a dress similar to this image, executes the call with stored credentials, and returns structured product results the agent can present to the user.

Through Jentic, search for visually similar fashion products and return the top three matches with image urls and product attributes

Key Endpoints

15 endpoints — jentic publishes the only available openapi specification for ibm research ai for fashion api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/catalog/{catalog_name}

Create a new fashion product catalog

POST

/catalog/{catalog_name}/products/{id}

Add a product to a catalog

POST

/catalog/{catalog_name}/visual_search_index

Build the visual search index for a catalog

POST

/catalog/{catalog_name}/visual_search

Find visually similar products from an uploaded image

GET

/catalog/{catalog_name}/text_search

Search catalog products by natural-language query

GET

/catalog/{catalog_name}/visual_browse/{id}/{image_id}

Browse visually related products from a catalog item

POST

/catalog/{catalog_name}

Create a new fashion product catalog

POST

/catalog/{catalog_name}/products/{id}

Add a product to a catalog

POST

/catalog/{catalog_name}/visual_search_index

Build the visual search index for a catalog

POST

/catalog/{catalog_name}/visual_search

Find visually similar products from an uploaded image

GET

/catalog/{catalog_name}/text_search

Search catalog products by natural-language query

Why though Jentic?

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

Credential isolation

The IBM Cognitive Fashion API key is stored encrypted in the Jentic vault. Agents receive scoped execution tokens — the raw API key is never exposed to model context or logs.

Intent-based discovery

Agents search Jentic by intent (e.g. find visually similar fashion products) and Jentic returns the matching IBM Fashion operation with its input schema, so the agent calls the right endpoint without browsing docs.

Time to first call

Direct integration: 1-3 days to wire auth, image upload handling, and indexing. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Clarifai API

General-purpose visual recognition with custom-trained fashion models

Choose Clarifai when you need broader computer vision tasks beyond fashion or want to train custom visual classifiers

Alternative

Imagga API

Image tagging, categorization, and visual similarity for any product imagery

Choose Imagga when you need general image tagging across categories rather than a fashion-specific catalog model

Complementary

Cloudinary Upload API

Store and transform the product images you index into the fashion catalog

Use alongside this API to host catalog product images and serve transformed variants to shoppers

FAQs

Specific to using IBM Research AI for Fashion API API through Jentic.

Why is there no official OpenAPI spec for IBM Research AI for Fashion API?

IBM does not publish an OpenAPI specification for this research API. Jentic generates and maintains this spec so that AI agents and developers can call the IBM Research AI for Fashion 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 IBM Research AI for Fashion API use?

The API uses an API key sent as a request header. When called through Jentic, the key is stored in the encrypted Jentic vault and is never exposed to the agent context — the agent receives a scoped execution token instead.

Can I run image-based search against my own product catalog with this API?

Yes. Create a catalog with POST /catalog/{catalog_name}, add products with POST /catalog/{catalog_name}/products/{id}, build the index with POST /catalog/{catalog_name}/visual_search_index, then call POST /catalog/{catalog_name}/visual_search with a customer image to retrieve visually similar items.

What are the rate limits for the IBM Research AI for Fashion API?

The OpenAPI spec does not declare per-endpoint rate limits. Limits are enforced by the IBM Cognitive Fashion service and depend on the tier provisioned for your API key — consult the service contract attached to your key for exact thresholds.

How do I run a fashion text search with this API through Jentic?

Install the SDK with pip install jentic, then search Jentic for the operation using a query like search fashion catalog by text, load the schema for GET /catalog/{catalog_name}/text_search, and execute the call with your catalog name and query string.

Does the API support visual browsing from an existing catalog item?

Yes. GET /catalog/{catalog_name}/visual_browse/{id}/{image_id} returns visually related products from a specific image attached to a catalog product, which is useful for related-item recommendations on product detail pages.