Install Jentic One Beta

Connect the Kubflow API to your agent

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

List available Kubflow models filtered by category: image, video, or audio

Read a single model's input schema, price, and prompting guide before writing the prompt

Get the exact credit cost of a generation for a given model, prompt, and params without starting or charging anything

Start an image or video generation with a model, prompt, and model-specific params, capped by max_credits

GET STARTED

Start building with Kubflow API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Kubflow API for, with concrete tasks.

★ Budget-Capped Image Generation

A content agent produces marketing images without overspending. It reads the chosen model's input schema and prompting guide, calls the price operation to get the exact credit cost for its prompt and params, and then starts the generation with max_credits set so Kubflow refuses the job if the price is higher than the cap.

Call GET /api/v1/models/{model_id} for the chosen image model, POST /api/v1/generations/price with model and prompt 'minimalist product shot of a ceramic mug on oak', then POST /api/v1/generations with the same body and max_credits set to the quoted price

Image-to-Video from a Reference

To animate an existing asset, the agent uploads the reference file through the uploads operation, which spends no generation credits, then starts a video generation that points at the uploaded file through the model's params. Because generation runs asynchronously, the agent polls the generation by id until the status reports a finished result.

Upload a PNG with POST /api/v1/uploads, start a video generation with POST /api/v1/generations using a video model and the upload reference in params, then poll GET /api/v1/generations/{generation_id} until it completes

Model Selection by Category

Kubflow exposes several models per medium. An agent filters the model list by category (image, video, or audio), reads each candidate's price and input schema, and picks the one that fits the task and budget before writing the prompt.

Call GET /api/v1/models with category 'audio', then GET /api/v1/models/{model_id} for each result and pick the lowest-priced model that accepts a text prompt

AI Agent Media Generation via Jentic

An AI agent reaches Kubflow through Jentic without handling the account API key. It searches by intent, receives the generation or pricing operation with its input schema, and executes with the bearer key injected at run time, so producing an image or clip is one step inside a larger content workflow.

Search Jentic for 'generate an image from a prompt', load the createGeneration schema, and execute it with a Kubflow model id, a prompt, and a max_credits cap

Key Endpoints

6 endpoints — the kubflow api generates images, videos, and audio from a text prompt using the models available on a kubflow account.

METHOD

PATH

DESCRIPTION

POST

/api/v1/generations

Start a generation

POST

/api/v1/generations/price

Get the exact credit cost

GET

/api/v1/generations/{generation_id}

Check status and result

GET

/api/v1/models

List models

GET

/api/v1/models/{model_id}

Get a model's schema and price

POST

/api/v1/uploads

Upload a reference file

POST

/api/v1/generations

Start a generation

POST

/api/v1/generations/price

Get the exact credit cost

GET

/api/v1/generations/{generation_id}

Check status and result

GET

/api/v1/models

List models

GET

/api/v1/models/{model_id}

Get a model's schema and price

POST

/api/v1/uploads

Upload a reference file

Why Jentic?

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

Setup

Wiring Kubflow by hand means sending the account key as a bearer token, pricing each generation, and polling asynchronous jobs until they finish. Through Jentic you install once, import Kubflow from the API Directory, store the key once, and your agent calls it.

Permission scoping

Kubflow carries the model, prompt, and params in the request body, so rules bound which operations your agent may call, not which model it uses. Limit it to the operations it needs, such as model reads and pricing, and leave out generation creation and uploads unless you add them.

Credential isolation

Your Kubflow 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 'generate an image from a prompt' or 'price a video generation', and Jentic returns the matching Kubflow operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Kubflow API through Jentic.

What authentication does the Kubflow API use?

Per its OpenAPI spec, the Kubflow API uses the bearerAuth scheme: your Kubflow account API key sent as a bearer token in the Authorization header. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at execution time, so it never enters the agent's context.

Can I find out what a generation costs before running it with the Kubflow API?

Yes. POST /api/v1/generations/price takes the same body as a real generation (model, prompt, optional category and params) and returns the exact credits it would cost without starting or charging anything. When you then start the generation, set max_credits so Kubflow refuses to start it if the price is higher.

What are the rate limits for the Kubflow API?

The OpenAPI spec does not specify rate limits. Check https://kubflow.ai for current limits. Generations are charged in credits, so use the price operation and max_credits to control spend.

How do I generate an image through Jentic with the Kubflow API?

Search Jentic for 'generate an image from a prompt' and it returns the createGeneration operation with its input schema. Execute POST /api/v1/generations with a model id from the models list and your prompt, then poll GET /api/v1/generations/{generation_id} for the result. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there a Kubflow MCP server?

You don't need an MCP server to give your agent the Kubflow API. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it. Operations are discovered by intent on demand, so no extra tool definitions are loaded into the agent's context.

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

Yes. For a research or planning agent, allow only the model reads and the price operation: it can browse models and quote costs but cannot start a generation that spends credits. Add the create and status operations when you want it to generate, and set max_credits on each request to cap the cost of any single job.

Kubflow API

AI ReadinessAI-Aware (B-)63/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLMl Inferencebearer6 EndpointsREST

For Agents

Pick a Kubflow model, price a prompt in credits, start an image, video, or audio generation with a spending cap, and poll until the result is ready.

Use for: Generate a product photo from this prompt, List all video models I can use on Kubflow, Check how many credits this generation will cost before I run it, Get the prompting guide for a specific Kubflow model

Not supported: Does not handle image editing pipelines, video hosting, or model training and fine-tuning. Use for pricing and running Kubflow image, video, and audio generations only.

The Kubflow API generates images, videos, and audio from a text prompt using the models available on a Kubflow account. A caller lists models by category, reads one model's input schema, price, and prompting guide, gets the exact credit cost of a generation before starting it, then starts the job and polls its status and result by generation id. Reference images, videos, or audio files can be uploaded first without spending generation credits, and a max_credits ceiling refuses any generation priced above it.

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.

33%

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: DSignals: 4

29%

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.

46%

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).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: B-Signals: 1

60%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Kubflow API to your agent

Capabilities

Start building with Kubflow API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Kubflow API

Jentic AI Readiness Score

Kubflow 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