Install Jentic One Beta

Connect the FASHN API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the FASHN 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%2Ffashn.ai%2Ffashn" | 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%2Ffashn.ai%2Ffashn" | 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 FASHN API.

Submit virtual try-on jobs that place a garment image onto a model image via POST /v1/run

Run AI fashion-model generation by selecting the appropriate model_name in POST /v1/run

Apply image-editing models (background, garment swap, retouching) through the same /v1/run endpoint

Poll for prediction completion and retrieve output URLs via GET /v1/status/{id}

GET STARTED

Start building with FASHN API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use FASHN API for, with concrete tasks.

★ Virtual Try-On for E-Commerce Product Pages

Fashion retailers use FASHN to render every garment on a consistent set of model images without booking studio shoots. The agent submits a try-on job to /v1/run with the garment URL, model image, and chosen model_name, then polls /v1/status/{id} for the rendered output. Cuts product-photography turnaround from weeks to minutes per SKU.

POST to /v1/run with model_name 'tryon' and a garment plus model image, then poll GET /v1/status/{id} until status is complete and return the output URL

AI Model Generation for Brand Lookbooks

Brands generate diverse model imagery without casting calls by calling /v1/run with a model-generation model_name and prompt. The async job returns rendered model photos suitable for lookbooks and marketing assets. Pairs well with image-editing follow-up calls to refine output.

POST to /v1/run with the model-generation model_name and a styling prompt, poll /v1/status/{id}, and store the resulting image URLs in the brand asset library

Credit-Aware Batch Pipeline

Agencies running large try-on batches first call GET /v1/credits to confirm balance, then submit jobs to /v1/run in parallel and track each via /v1/status/{id}. The pattern prevents mid-batch failures from credit exhaustion. Useful for any workflow that needs to plan against a metered AI budget.

Call GET /v1/credits to check balance, then for N jobs POST /v1/run and poll /v1/status/{id} for each prediction id

AI Stylist Agent via Jentic

Conversational stylists embedded in shopping apps use Jentic to call FASHN without managing bearer tokens directly. The agent searches 'generate virtual try-on', loads /v1/run, and orchestrates the predict-poll loop using the credentials Jentic injects. Cuts integration from days to under an hour.

Through Jentic, search 'generate a virtual try-on', load /v1/run, submit the user's chosen garment and model image, then poll /v1/status/{id} until the result is ready

Key Endpoints

3 endpoints — the fashn api runs virtual try-on, model creation, and image-editing models from the fashn ai platform.

METHOD

PATH

DESCRIPTION

POST

/v1/run

Submit a prediction job for the chosen model_name

GET

/v1/status/{id}

Poll for prediction status and retrieve output

GET

/v1/credits

Get remaining credit balance

POST

/v1/run

Submit a prediction job for the chosen model_name

GET

/v1/status/{id}

Poll for prediction status and retrieve output

GET

/v1/credits

Get remaining credit balance

Why Jentic?

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

Setup

Wiring the FASHN API by hand means learning its bearer auth and running the async pattern yourself: POST a run, then poll /v1/status/{id} until the render finishes. Through Jentic you install once, import FASHN from the API Directory, store the key once, and your agent calls it.

Permission scoping

FASHN is built around a small set of operations for try-on rendering, so limit the agent to the operations it needs, such as starting a run, checking a run status, or reading the remaining credit balance. You choose which of those operations it may call, so the run trigger is included only when you add it.

Credential isolation

Your FASHN bearer token 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 'generate a virtual try-on', and Jentic returns the POST /v1/run operation with its model_name input schema so the agent picks the right model variant without browsing the FASHN docs.

FAQs

Specific to using FASHN API through Jentic.

What authentication does the FASHN API use?

FASHN uses HTTP Bearer token authentication - supply your FASHN API key in the Authorization header as a bearer token. Through Jentic the bearer token is stored encrypted in the vault and injected at execution time.

Can I run virtual try-on with the FASHN API?

Yes. POST to /v1/run with the try-on model_name plus garment and model image inputs, then poll GET /v1/status/{id} until the prediction completes and returns the rendered output URL.

What are the rate limits for the FASHN API?

FASHN does not publish explicit rate limits in the OpenAPI spec - usage is governed by the credit balance returned by /v1/credits. Check credits before large batches and back off on 429 responses if encountered.

How do I generate a try-on image through Jentic?

Install with pip install jentic, search 'generate a virtual try-on', load POST /v1/run, execute with model_name and image inputs, then poll /v1/status/{id} until complete. Get started with Jentic One, the self-hosted execution layer.

Are predictions synchronous or async on the FASHN API?

Async. POST /v1/run returns a prediction id immediately and the agent polls GET /v1/status/{id} until status reports complete. Plan your agent loop with a poll interval and timeout.

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

Yes. Because you run Jentic One yourself, your own rules decide which FASHN operations and credentials the agent may use. You can grant read-only access to checking run status via GET /v1/status/{id} and reading the credit balance via GET /v1/credits, while withholding the ability to start new predictions with POST /v1/run. The run trigger is available to the agent only when you choose to include it.

FASHN API

AI ReadinessAI-Aware (A-)72/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLVisionbearer3 EndpointsREST

For Agents

Submit virtual try-on, model generation, and AI image-editing jobs to FASHN, then poll for results and check remaining credits.

Use for: Generate a virtual try-on of a garment on a model image, Create an AI fashion model image for a product shoot, Edit a product photo to swap the background, Check the status of a FASHN prediction job

Not supported: Does not handle physical product fulfilment, sizing recommendations, or full e-commerce flows - use for AI image generation and try-on rendering only.

The FASHN API runs virtual try-on, model creation, and image-editing models from the FASHN AI platform. Predictions are submitted through a single universal /v1/run endpoint with a model_name field, then polled for completion via /v1/status/{id}. A /v1/credits endpoint reports remaining credit balance. The async predict-then-poll pattern lets agents submit jobs without holding open connections.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

99%

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).

75%

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

82%

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.

50%

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: B-Signals: 1

60%

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 FASHN API to your agent

Capabilities

Start building with FASHN API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

FASHN API

Jentic AI Readiness Score

FASHN 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