Install Jentic One Beta

Connect the Mangii Headless API to your agent

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

Start a new illustrated story from a prompt and style_id, with quality set to standard, hd, or ultra

Continue an existing story with a new panel, reusing identity_panel_id or continuity_panel_id to keep characters consistent

Regenerate a single panel with a revised prompt without rebuilding the whole story

Generate a standalone image in any canonical style returned by the styles listing

GET STARTED

Start building with Mangii Headless API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Mangii Headless API for, with concrete tasks.

★ Agent-driven comic creation through Jentic

A creative agent turns a user's idea into a multi-panel comic by calling the Mangii Headless API through Jentic. It lists styles, creates the story with a prompt and style_id, polls the returned job_id until the job succeeds, then appends panels one at a time. Jentic injects the mangii_sk_ bearer key at call time, so the agent never handles the key itself.

Call GET /v1/styles, pick a style id, POST /v1/stories with prompt 'A robot learns to paint in a rainy city' and that style_id, then poll GET /v1/jobs/{id} with the returned job_id until status is succeeded

Consistent characters across panels

Story tools need the same character to look the same from panel to panel. The continue-story operation accepts identity_panel_id, identity_image, continuity_panel_id, and context_panel_ids, so each new panel can anchor on an earlier one or on a supplied identity photo. This keeps faces and outfits stable while the scene changes.

POST /v1/stories/{id}/panels with prompt 'She opens the old door and finds a garden' and identity_panel_id set to the first panel's id, then GET /v1/stories/{id}/panels to confirm the new panel was added

Fixing a single panel

When one frame misses the brief, regenerating it on its own avoids paying to rebuild the story. The regenerate operation takes a revised prompt and the same continuity inputs as a new panel, and unwanted panels can be deleted outright. Credits charged and remaining are returned with each accepted job.

POST /v1/stories/{id}/panels/{pid}/regenerate with prompt 'Same scene but at sunset' and quality 'hd', then poll GET /v1/jobs/{id} until the regenerated panel is ready

Credit-aware generation budgeting

Generation spends credits, so an agent should know the balance before queuing hd or ultra work. The credits call returns weeklyCredits, packCredits, apiPackCredits, and spendableCredits, and the usage call returns per-day consumption. When a top-up is needed, the checkout operation returns a Stripe Checkout URL for a human to complete.

Call GET /v1/credits, read spendableCredits, and only POST /v1/images with quality 'ultra' when the balance covers it; otherwise call GET /v1/usage and report the last days of spend

Key Endpoints

16 endpoints — the mangii headless api generates illustrated comic stories and single images from text prompts, then lets you extend a story panel by panel.

METHOD

PATH

DESCRIPTION

POST

/v1/stories

Create a story (async by default)

POST

/v1/stories/{id}/panels

Continue a story with a new panel

POST

/v1/stories/{id}/panels/{pid}/regenerate

Regenerate one panel

POST

/v1/images

Generate a standalone image

GET

/v1/jobs/{id}

Poll a generation job

GET

/v1/styles

List canonical styles

GET

/v1/credits

Read credit balances

POST

/v1/stories

Create a story (async by default)

POST

/v1/stories/{id}/panels

Continue a story with a new panel

POST

/v1/stories/{id}/panels/{pid}/regenerate

Regenerate one panel

POST

/v1/images

Generate a standalone image

GET

/v1/jobs/{id}

Poll a generation job

GET

/v1/styles

List canonical styles

GET

/v1/credits

Read credit balances

Why Jentic?

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

Setup

Wiring Mangii by hand means handling its mangii_sk_ bearer key, polling asynchronous jobs, and tracking which credit pools a call spends. Through Jentic you install once, import the Mangii Headless API from the API Directory, store the key once, and your agent calls it.

Permission scoping

Mangii puts the story id in the URL path (/v1/stories/{id}/...), so a rule can pin your agent to one story and the operations you pick under it. You choose the operations it may call, so destructive ones like story deletion or panel deletion and the checkout calls are not included unless you add them.

Credential isolation

Your Mangii 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 'create a comic story' or 'regenerate a panel', and Jentic returns the matching Mangii operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Mangii Headless API through Jentic.

Is there a Mangii MCP server?

You don't need an MCP server to give your agent the Mangii Headless API. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it. Operations are found by intent when needed, so no extra tool definitions sit in the agent's context.

What authentication does the Mangii Headless API use?

Per its OpenAPI spec, the Mangii Headless API authenticates with a bearer token in the Authorization header, using a key that starts with mangii_sk_. The spec notes that the checkout operations need a key with billing:write scope, which generate-scoped keys lack. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time.

How do generation jobs work in the Mangii Headless API?

Story, panel, regenerate, and image calls run asynchronously by default and return 202 with a job_id, the credits charged, and a next_step hint. Poll GET /v1/jobs/{id} until status moves from queued or running to succeeded or failed. Set async to false in the request body if you want a synchronous response instead.

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

Yes. Write a rule that allows only GET /v1/styles, POST /v1/stories, POST /v1/stories/{id}/panels, and GET /v1/jobs/{id}, so the agent can make and extend stories and wait for jobs but cannot delete stories or panels or open a checkout session. Every call it makes runs through your own instance.

What are the rate limits for the Mangii Headless API?

The OpenAPI spec does not state a numeric limit. Responses carry X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset headers, and a throttled call returns 429, so read those headers to pace generation. See https://api.mangii.ai/openapi.json for the current published spec.

How do I create a comic story with the Mangii Headless API through Jentic?

Add the Mangii Headless API to Jentic One from the directory and store your mangii_sk_ key once. Your agent then searches for 'create a comic story from a prompt', calls POST /v1/stories with a prompt and style_id, and polls GET /v1/jobs/{id} with the returned job_id. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Mangii Headless API

AI ReadinessFoundational (C+)59/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLVisionbearer16 EndpointsREST

For Agents

Generate comic stories and images from prompts in a chosen Mangii style, add or regenerate panels, poll generation jobs, and check the credit balance before spending.

Use for: I want to turn a short story idea into a comic with a consistent main character, Add the next panel to my Mangii story where the hero reaches the castle, Regenerate panel three with a darker night-time scene, List all available Mangii art styles

Not supported: Does not handle video animation, print fulfilment, or publishing comics to a storefront. Use for generating illustrated stories, panels, and images from prompts only.

The Mangii Headless API generates illustrated comic stories and single images from text prompts, then lets you extend a story panel by panel. A story starts from a prompt and a style_id, optional identity or reference images keep a character consistent, and each panel can be regenerated or deleted on its own. Generation calls run asynchronously by default and return a job_id you poll until the job succeeds, while separate billing calls report credit balances, per-day usage, and Stripe Checkout links for buying more credits.

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

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).

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: BSignals: 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: FSignals: 4

47%

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.

10%

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: DSignals: 1

45%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Mangii Headless API to your agent

Capabilities

Start building with Mangii Headless API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Mangii Headless API

Jentic AI Readiness Score

Mangii Headless 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