Install Jentic One Beta

Connect the Label Studio API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Label Studio 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%2Flabelstud.io%2Flabelstud" | 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%2Flabelstud.io%2Flabelstud" | 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 Label Studio API.

Create and list labeling projects and read their task counts

List and create tasks within a project for annotators to label

Collect annotations on a task and update or delete them

List and create model predictions to pre-label tasks or measure agreement

Import tasks and prediction files into a project

GET STARTED

Start building with Label Studio API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Label Studio API for, with concrete tasks.

★ AI Labeling Orchestrator via Jentic

An AI agent can drive a Label Studio labeling loop without a developer wiring the API by hand. Through Jentic the agent searches for the operation it needs by intent, receives the matching Label Studio endpoint and its input schema, and calls it with the token injected at execution time. This lets a data team delegate task loading, prediction upload, and export to an agent that works directly against live projects.

Search Jentic for 'import tasks into a project', load the operation schema, call POST /api/projects/{id}/import, then poll task counts

Model-Assisted Pre-Labeling

Teams that already have a model can pre-label tasks so annotators only correct instead of labeling from scratch. The Label Studio API creates predictions against tasks, and annotators then confirm or fix them, cutting the manual effort. This raises throughput on large datasets where the model is already reasonably accurate.

Call POST /api/predictions/ for each task with the model's output, then let annotators review them in the project

Training Data Export Pipeline

Once a project is annotated, an ML pipeline needs the labeled data in a training-ready format. The Label Studio API triggers and retrieves exports so a scheduled job can pull the latest labels whenever a project reaches a threshold. Because the export comes straight from the platform, the training set always reflects the current annotations.

Call POST /api/projects/{id}/exports/ to create an export, then GET the snapshot when it is ready

Key Endpoints

397 endpoints — the label studio api manages data labeling projects for machine learning teams, exposing projects, tasks, annotations, predictions, and exports over a rest interface.

METHOD

PATH

DESCRIPTION

GET

/api/projects/

List labeling projects

POST

/api/projects/

Create a project

GET

/api/tasks/

List tasks

POST

/api/tasks/{id}/annotations/

Create an annotation on a task

GET

/api/predictions/

List predictions

POST

/api/predictions/

Create a prediction

POST

/api/projects/{id}/import

Import tasks into a project

GET

/api/projects/{id}/export

Export a project's annotations

GET

/api/projects/

List labeling projects

POST

/api/projects/

Create a project

GET

/api/tasks/

List tasks

POST

/api/tasks/{id}/annotations/

Create an annotation on a task

GET

/api/predictions/

List predictions

POST

/api/predictions/

Create a prediction

POST

/api/projects/{id}/import

Import tasks into a project

GET

/api/projects/{id}/export

Export a project's annotations

Why Jentic?

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

Setup

Wiring the Label Studio API by hand means setting the Authorization token on every request and threading project, task, and export ids through the right endpoints yourself. Through Jentic you install once, import Label Studio from the API Directory, store the token once, and your agent calls it.

Permission scoping

Label Studio puts the record id in the URL path (/api/projects/{id}/, /api/tasks/{id}/), so a rule can pin your agent to reading projects and adding predictions. You choose the operations it may call, so destructive operations like deleting annotations are not included unless you add them.

Credential isolation

Your Label Studio token 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 'import tasks into a project' or 'export labeled data', and Jentic returns the matching Label Studio operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Label Studio API through Jentic.

What authentication does the Label Studio API use?

The Label Studio API authenticates with a token sent in the Authorization header, as declared in its OpenAPI spec. Through Jentic the token is stored encrypted by your own Jentic One instance and injected at execution time, so it never enters the agent's prompt, logs, or context.

Can I upload model predictions with the Label Studio API?

Yes. Call POST /api/predictions/ to attach a model's output to a task, so annotators review and correct pre-labels instead of labeling from scratch. This is the standard path for model-assisted labeling on large datasets.

What are the rate limits for the Label Studio API?

The OpenAPI spec does not specify rate limits. Check the Label Studio API documentation at https://labelstud.io/guide/ for current limits before running high-volume import or export jobs.

How do I export labeled data through Jentic?

Search Jentic for 'export project annotations', which resolves to the Label Studio export operations, and Jentic returns their input schema so your agent can trigger and fetch a snapshot. Credentials are injected at call time from your own instance. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Can I restrict what my agent is allowed to do with the Label Studio API?

Yes. Write a rule that allows only the operations your agent needs, such as GET /api/projects/ and POST /api/predictions/, so it can read projects and add pre-labels but cannot delete annotations, and every call it makes is logged by your own instance. You add write or delete operations to the allowed set only when you decide to.

Is there a Label Studio MCP server?

You don't need an MCP server to give your agent Label Studio. Jentic connects it directly from the API Directory: import Label Studio, store your token once, and your agent calls the project, task, and prediction operations on demand without loading another server's tool definitions into its context.

Labelstud Label Studio API

Name: Label Studio API
Brand: Label Studio API
Availability: InStock

Official vendor OpenAPI document · agent-readyAI/MLMl InferenceapiKey397 EndpointsREST

For Agents

Create labeling projects, load tasks, collect annotations, attach predictions, and export labeled data in Label Studio. Covers 397 endpoints across projects, tasks, annotations, and predictions.

Use for: Create a new labeling project for an image classification dataset, Load a batch of tasks into an existing project, Collect the annotations recorded on a specific task, Attach model predictions to tasks so annotators can review them

Not supported: Does not train models or run inference itself. Use for managing labeling projects, annotations, and dataset exports only.

The Label Studio API manages data labeling projects for machine learning teams, exposing projects, tasks, annotations, predictions, and exports over a REST interface. It lets you create a project, load tasks, collect human annotations, attach model predictions, and export the labeled dataset for training. Requests carry a token in the Authorization header and return structured JSON that a pipeline can act on directly.

Jentic One on Github View OpenAPI Document

Connect the Label Studio API to your agent

Capabilities

Start building with Label Studio API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Labelstud Label Studio API