Install Jentic One Beta

Connect the Martini API to your agent

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

Submit a generation to a model endpoint returned by the model list, or save it as an uncharged draft with mode set to draft and start it later

Place a copy of a saved workflow into a project and start a run with variables, bins, and an oliveBudget cap

Poll a generation or run until it reaches completed, failed, or cancelled, using the wait parameter on run status to long-poll

Resume a failed workflow run and fetch its outputs once it finishes

GET STARTED

Start building with Martini API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Martini API for, with concrete tasks.

★ Agent-run media generation through Jentic

A coding or production agent can drive Martini end to end through Jentic: it lists the model endpoints the key may use, submits a generation with an idempotencyKey so a retried call never charges twice, and polls the generation until its status is terminal. Because Martini has no webhooks, the submit-then-poll loop is the whole integration, and the agent only needs the generation id to finish it.

Call the model list, pick an image-to-video endpoint, submit a generation to it with an idempotencyKey of trailer-shot-01 in project Launch Trailer, then poll the generation every few seconds until its status is completed and return the output

Repeatable workflow runs for a production pipeline

Teams that have built a multi-step Martini workflow can rerun it from code with fresh inputs. Starting a run on a saved workflow uses its saved definition and presets on a fresh canvas, variables are pinned by name, bins can be filled with public https URLs that Martini imports before the run starts, and an oliveBudget caps the credit spend. If a step fails, the run can be resumed rather than restarted.

Start a run of workflow wf_123 with the variable product_name set to Aurora and an oliveBudget of 500, poll its compact status with wait enabled until it is terminal, resume it once if it failed, then fetch the run results

Feeding source assets into a project

Generations and workflow runs often need reference images, footage, or audio. The Martini API offers a two-step direct upload (prepare, push the bytes, then complete) for larger files and a single base64 upload for small ones, each optionally placed on a canvas. The asset status route confirms a file is ready before it is used as an input.

Upload logo.png as base64 with contentType image/png into project Launch Trailer, then read the asset status and confirm it is ready before submitting a generation that uses it

Key Endpoints

25 endpoints — the martini api submits image, video, and audio generations to the models your key can invoke and runs saved martini workflows on a project canvas.

METHOD

PATH

DESCRIPTION

GET

/v1/models

List model endpoints the key can invoke

POST

/v1/generations

Submit a generation or draft

GET

/v1/generations/{id}

Generation status and output

POST

/v1/workflows/{workflowId}/runs

Start a workflow run

GET

/v1/runs/{runId}/status

Compact run status

GET

/v1/runs/{runId}/results

Run outputs

POST

/assets/uploads/base64

Upload one small file

GET

/v1/models

List model endpoints the key can invoke

POST

/v1/generations

Submit a generation or draft

GET

/v1/generations/{id}

Generation status and output

POST

/v1/workflows/{workflowId}/runs

Start a workflow run

GET

/v1/runs/{runId}/status

Compact run status

GET

/v1/runs/{runId}/results

Run outputs

POST

/assets/uploads/base64

Upload one small file

Why Jentic?

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

Setup

Wiring Martini by hand means handling its bearer key, an asynchronous submit-then-poll loop with no webhooks, idempotency keys for safe retries, and Retry-After backoff. Through Jentic you install once, import the Martini API from the API Directory, store the key once, and your agent calls it.

Permission scoping

Martini puts the workflow id, run id, and project id in the URL path (/v1/workflows/{workflowId}/runs, /v1/runs/{runId}), so a rule can pin your agent to one workflow and the operations you pick. You choose the operations it may call, so spending ones like submitting new generations or invoking tools are not included unless you add them.

Credential isolation

Your Martini 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 a video clip' or 'run a saved workflow', and Jentic returns the matching Martini operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Martini API through Jentic.

What authentication does the Martini API use?

The Martini API authenticates with a bearer token per its OpenAPI spec: send your Martini key as Authorization: Bearer followed by the key, and the Key form is accepted too. Keys are created under Settings in Martini and are server-side secrets. The CLI login pairing routes and the spec document route are open and need no key. Through Jentic the key is stored encrypted on your own instance and injected at call time.

Is there a Martini MCP server?

You don't need an MCP server to give your agent Martini. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it. Operations are discovered on demand, so your agent's context is not filled with another server's tool definitions.

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

Yes. For example, write a rule that allows only starting runs, reading run status, and reading run results under /v1/workflows/wf_123 and /v1/runs, so the agent can rerun that one workflow and collect its outputs but cannot submit ad hoc generations or upload assets. Every call it makes is logged.

How do I know when a Martini generation is finished?

Poll it. The Martini API sends no webhooks, so after submitting a generation you call GET /v1/generations/{id} until the status is completed, failed, or cancelled. Workflow runs work the same way through the run status routes, and the compact status route accepts a wait parameter so each poll can hold until something changes.

What are the rate limits for the Martini API?

The OpenAPI spec does not publish numeric rate limits. It says to retry only rate-limited and server-error responses, waiting for the Retry-After header, and rate-limited answers carry the stable RATE_LIMITED code. Check https://www.martini.film/docs/api for current limits.

How do I run a saved Martini workflow through Jentic?

Search Jentic for an intent like 'run a Martini workflow', import the Martini API, and store your key once. Your agent then lists workflows, calls POST /v1/workflows/{workflowId}/runs with its variables and an idempotencyKey, polls the run, and fetches the results. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Martini Film Martini API

AI ReadinessAI-Aware (B)65/100

See full scorecard

Official vendor OpenAPI document · agent-readyMediaVideo Processingbearer25 EndpointsREST

For Agents

Submit AI media generations, run saved Martini workflows, upload source assets into projects, and poll each job until it finishes. Prices are in olives and idempotency keys prevent double charges on retries.

Use for: I need to generate a video clip with a Martini model, List the model endpoints my Martini key can submit to, Run my saved storyboard workflow on a new canvas, Check whether my Martini generation has finished

Not supported: Does not handle video editing timelines, hosting or streaming finished video, or webhook notifications. Use for submitting Martini generations and workflow runs and collecting their outputs only.

The Martini API submits image, video, and audio generations to the models your key can invoke and runs saved Martini workflows on a project canvas. Generations and workflow runs are asynchronous: you submit, then poll the status route until it reports completed, failed, or cancelled, since the API sends no webhooks. An idempotencyKey on generations and runs returns the same object on a retry instead of charging twice, and prices are quoted in olives, Martini's credit unit. Direct and base64 asset uploads feed source files into projects, and a tool manifest exposes the same tools Martini offers to agents.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 4

97%

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).

42%

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.

70%

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

31%

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.

52%

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: A-Signals: 1

71%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Martini API to your agent

Capabilities

Start building with Martini API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Martini Film Martini API

Jentic AI Readiness Score

Martini 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