Install Jentic One Beta

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.

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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fcognitivefashion.github.io%2Fibm-ai-for-fashion-api" | sh

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%2Fcognitivefashion.github.io%2Fibm-ai-for-fashion-api" | 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.

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

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use IBM Research AI for Fashion 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

GET

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

Browse visually related products from a catalog item

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the IBM Research AI for Fashion API by hand means sending its API-key header, targeting api.cognitivefashion.com/v1, and threading the catalog name through every index, search, and browse call yourself. Through Jentic you install once, import the IBM Research AI for Fashion API from the API Directory, store the key once, and your agent calls it.

Permission scoping

The Fashion API puts the catalog name in the URL path (/catalog/{catalog_name}/...), so a rule can pin your agent to one catalog: it builds the visual search index, runs visual and text search, and browses products within that catalog and nothing else. You choose the operations it may call, so adding or overwriting products is only in the allowed set if you add it.

Credential isolation

Your IBM Cognitive Fashion 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 'find visually similar fashion products' or 'search a fashion catalog by text', and Jentic returns the matching operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using IBM Research AI for Fashion 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 with Jentic One, the self-hosted execution layer.

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 your encrypted Jentic One instance 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.

Can I limit what my agent is allowed to do with the IBM Research AI for Fashion API?

Yes. Because Jentic One is self-hosted, your own rules decide which operations and credentials the agent may use. Since the catalog name sits in the URL path (/catalog/{catalog_name}/...), you can pin the agent to a single catalog and allow only the operations you choose, such as building the visual search index and running visual search, text search, and visual browse within that catalog. Write operations like adding or overwriting products are included only if you explicitly add them to the allowed set.

Cognitivefashion IBM Research AI for Fashion API

AI ReadinessAI-Aware (B)67/100

See full scorecard

Agent-ready OpenAPI document · curated by JenticAI/MLVisionapiKey15 EndpointsREST

Know of an official OpenAPI document? Contribute it →

For Agents

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

Use for: I want to index a new fashion product catalog for visual search, Search for visually similar dresses to an uploaded image, Find all products in the catalog matching the phrase floral summer dress, Retrieve the product details stored under a given catalog id

Not supported: Does not handle payment processing, order fulfillment, or general image recognition outside fashion - use for fashion product catalog and visual or text search only.

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.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 4

98%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

42%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

50%

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.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: C-Signals: 4

38%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

65%

Summary Coverage

Coverage of summaries across operations/tags/info.

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.

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.

Connect the IBM Research AI for Fashion API to your agent

Capabilities

Start building with IBM Research AI for Fashion API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Cognitivefashion IBM Research AI for Fashion API

Jentic AI Readiness Score

IBM Research AI for Fashion API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness