FASHN API

Name: FASHN API
Brand: FASHN API
Availability: InStock

✓ Official Vendor SpecAI/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.

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://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 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

GET STARTED

Start building with FASHN API

Explore with Jentic

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?

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

Credential isolation

FASHN bearer tokens are stored encrypted in the Jentic vault. Agents receive scoped execution access — the raw token never enters prompts, logs, or model context.

Intent-based discovery

Agents search '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 FASHN docs.

Time to first call

Direct integration: 1-2 days for bearer auth, the predict-poll loop, and credit handling. Through Jentic: under an hour — search, load, execute.

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. Sign up at https://app.jentic.com/sign-up.

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.