Install Jentic One Beta

Connect the Deep Art Effects to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Deep Art Effects, 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%2Fdeeparteffects.com%2Fdeeparteffects-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%2Fdeeparteffects.com%2Fdeeparteffects-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 Deep Art Effects API.

List available artistic styles via GET /noauth/styles

Submit an image and chosen style for processing through POST /noauth/upload

Retrieve the stylised output once processing completes with GET /noauth/result

Authenticate every call with the x-api-key header

Drive a submit-and-poll style-transfer pipeline that fits inside an async media workflow

GET STARTED

Start building with Deep Art Effects API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Deep Art Effects API for, with concrete tasks.

★ Stylised user content in mobile and web apps

Apps that let users transform photos into art-style variants need a stable catalogue and a submit-and-poll flow. /noauth/styles returns the catalogue, /noauth/upload accepts the user image with a chosen style, and /noauth/result returns the rendered output. The split between submit and result lets the client show progress while the GPU job runs.

GET /noauth/styles, render the picker, POST /noauth/upload with the user's image and chosen style id, then poll /noauth/result for the rendered output.

Brand-locked editorial illustration generation

Editorial workflows that want a consistent visual treatment across a content series can lock in a single style id from /noauth/styles and apply it to every source photo through /noauth/upload. Because the same style id renders the same look, the published series stays visually coherent without per-piece manual editing.

POST /noauth/upload for each editorial photo using the locked-in style id, then GET /noauth/result for each job and store the output URLs.

Style catalogue refresh on a schedule

Integrations that surface a style picker should keep it in sync with the live catalogue. Calling /noauth/styles on a daily schedule lets the client pick up any new or seasonal styles without redeploying. This is especially useful when Deep Art Effects launches limited-time styles tied to events or holidays.

GET /noauth/styles each day and diff the response against the cached catalogue to pick up new styles.

AI agent stylising photos through Jentic

An agent producing stylised creative for a campaign can search Jentic for 'apply artistic style to a photo', list styles via Jentic, and execute the upload-and-result pair without holding the API key. Because the catalogue and the result endpoint are separate, the agent can also branch logic on which styles are available before submitting.

Search Jentic for 'apply artistic style to a photo', GET /noauth/styles, POST /noauth/upload with the chosen style and image, then GET /noauth/result.

Key Endpoints

3 endpoints — deep art effects is an ai artistic style transfer service whose three-endpoint api lets a client list available styles, upload an image with a chosen style, and retrieve the rendered output.

METHOD

PATH

DESCRIPTION

GET

/noauth/styles

List available artistic styles

POST

/noauth/upload

Submit an image with a chosen style

GET

/noauth/result

Retrieve the stylised result

GET

/noauth/styles

List available artistic styles

POST

/noauth/upload

Submit an image with a chosen style

GET

/noauth/result

Retrieve the stylised result

Why Jentic?

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

Setup

Wiring the Deep Art Effects API by hand means learning its x-api-key header and building the flow across its /noauth style, upload, and result endpoints yourself. Through Jentic you install once, import the Deep Art Effects API from the API Directory, store the API key once, and your agent calls it.

Permission scoping

Deep Art Effects carries the image and style choice in the request rather than as a resource id in the URL path, so limit the agent to the operations it needs, such as listing styles, uploading an image, and reading the result. Because you pick the operations, the agent runs only the style-transfer calls you grant.

Credential isolation

Your Deep Art Effects x-api-key is stored once, encrypted, by your own Jentic One instance and set in the header at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'apply artistic style to a photo' or 'list available art styles', and Jentic returns the matching operation with its input schema so the agent calls the right endpoint without learning the /noauth path quirk by hand.

FAQs

Specific to using Deep Art Effects API through Jentic.

Which OpenAPI specification does this Deep Art Effects page describe?

A curated, agent-optimized Jentic specification covering the three Deep Art Effects operations. Deep Art Effects also publishes its own Swagger 2.0 document in its documentation at http://docs.deeparteffects.com/swagger.json, which has been unchanged since 13 September 2017 and carries neither operation ids nor per-operation descriptions. The Jentic variant is a validated OpenAPI 3.0.0 conversion of that same document, matching it operation for operation and adding the operation ids and descriptions agent tooling needs. Get started with Jentic One, the self-hosted execution layer.

What authentication does the Deep Art Effects API use?

Deep Art Effects uses an API key passed in the x-api-key header. Despite the /noauth path prefix, every endpoint requires the key. Through Jentic the key is held in the encrypted vault and applied to the header at execution time, so it never enters the agent's context.

How do I find the right style id before uploading?

GET /noauth/styles returns the catalogue of available styles with their identifiers. Cache the result on the client to populate a picker, and pass the chosen style id in the /noauth/upload request body.

How do I get the stylised output after submitting an image?

Call GET /noauth/result after /noauth/upload completes. Heavier styles can take several seconds to render, so build retry-with-backoff into the polling client rather than calling once and giving up.

What are the rate limits for the Deep Art Effects API?

The OpenAPI spec does not document explicit rate limits. Treat /noauth/styles as cacheable client-side and reserve /noauth/upload for actual user-driven submissions to keep request volume bounded.

How do I apply style transfer through Jentic?

Run pip install jentic, search for 'apply artistic style to a photo', and Jentic returns the /noauth/upload operation with its input schema. List styles via /noauth/styles, load /noauth/upload with the chosen style id and image, execute, then poll /noauth/result.

Can I limit what my agent is allowed to do with the Deep Art Effects API?

Yes. Because you run Jentic One yourself, your own rules decide which of the Deep Art Effects operations the agent may call and which credentials it may use. You can grant only the calls a given task needs, such as listing styles with GET /noauth/styles, submitting an image with POST /noauth/upload, and reading the output with GET /noauth/result, and withhold the rest. Since the image and chosen style travel in the request body rather than as a resource id in the URL path, scoping happens at the operation level so the agent runs only the style-transfer calls you allow.

Deep Art Effects

Browse all Deeparteffects APIs

AI ReadinessFoundational (C+)60/100

See full scorecard

Official vendor OpenAPI document · agent-readyMediaImage ProcessingapiKey3 EndpointsREST

For Agents

Apply AI artistic style transfer to images by listing styles, submitting an image, and retrieving the stylised result via the Deep Art Effects API.

Use for: I need to apply a Picasso-style filter to a photo, List all artistic styles offered by Deep Art Effects, Submit an image for AI style transfer, Retrieve the result of a Deep Art Effects style transfer job

Not supported: Does not handle background removal, super resolution, or text-to-image generation - use for Deep Art Effects artistic style transfer only.

Deep Art Effects is an AI artistic style transfer service whose three-endpoint API lets a client list available styles, upload an image with a chosen style, and retrieve the rendered output. Jentic maintains a curated, agent-optimized OpenAPI 3.0.0 specification converted from the Swagger 2.0 document Deep Art Effects publishes on its own documentation host, with operation ids and per-operation descriptions added for agent tooling. The submit-then-poll pattern keeps client logic simple while supporting the heavy GPU work that style transfer requires. Authentication is an x-api-key header.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

100

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).

100%

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: C+Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

42%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

93%

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: FSignals: 4

46%

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.

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 Deep Art Effects to your agent

Capabilities

Start building with Deep Art Effects API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Deep Art Effects

Jentic AI Readiness Score

Deep Art Effects

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