Google Data Labeling API
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For Agents
Submit raw data to Google's Data Labeling Service, manage labeling jobs, and export human-labeled datasets for ML training. Lets agents drive a labeling pipeline without operating a workforce.
Use for: I need to submit an image classification labeling job for 5,000 photos, Create a dataset and import images from a Cloud Storage bucket, Export the annotated dataset for training a Vertex AI model, List all labeling jobs that are currently in WAITING state
Not supported: Does not train ML models, run inference, or host model endpoints - use for dataset import, human-labeled annotation jobs, and labeled-data export only.
The Google Cloud Data Labeling API exposes the public surface of the Data Labeling Service for AI training data preparation. It covers 28 endpoints for creating datasets, importing raw data, requesting human-labeled annotations across image, video, and text tasks, and exporting annotated datasets back to Cloud Storage. The service is designed for teams who need labeled corpora for supervised ML model training but do not want to run their own annotation pipeline.
Install Jentic One Beta
Connect the Data Labeling API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Data Labeling 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.
1
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%2Fgoogleapis.com%2Fdatalabeling" | sh2
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%2Fgoogleapis.com%2Fdatalabeling" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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 Data Labeling API.
Create datasets and import images, video, or text from Cloud Storage
Submit image, video, and text labeling jobs to Google's annotation workforce
Track labeling job status, pause, resume, and cancel running jobs
Export annotated datasets to Cloud Storage in standard ML training formats
Define annotation specs and instruction documents that guide human labelers
Inspect AnnotatedDatasets and individual examples to audit label quality
Use Cases
Patterns agents use Data Labeling API for, with concrete tasks.
★ Bootstrap a Supervised Training Dataset
Submit raw images, video, or text from Cloud Storage and receive a fully annotated dataset back, ready to train a Vertex AI or custom TensorFlow model. The API handles importData, requesting a labeling job with an annotation spec, and exportData when the job completes. End-to-end turnaround is typically days rather than the weeks needed to operate an in-house annotation team.
Create a dataset 'cat-dog-photos', import images from gs://acme-ml/raw/, submit an image classification labeling job with an annotation spec of CAT and DOG labels, and export the result when the job completes
Active Learning Loop with Human Labels
Use the Data Labeling Service inside an active learning loop: a model identifies low-confidence predictions, those examples are submitted as a new labeling job, and the resulting AnnotatedDataset feeds back into model retraining. The API's pause/resume/cancel controls let the loop adjust labeling instructions or stop early if the model's accuracy plateaus.
Submit a labeling job for 500 low-confidence predictions exported by the previous training run, monitor the operation, and merge the new AnnotatedDataset into the existing dataset
Audit and Export Labeled Datasets
Inspect existing annotated datasets to verify label coverage, sample examples, and confirm annotation spec consistency before exporting. The API exposes list and get operations on AnnotatedDatasets, Examples, and AnnotationSpecs, plus an exportData operation that writes results back to Cloud Storage in JSONL format. This supports compliance reviews and dataset versioning workflows.
List all AnnotatedDatasets under dataset 'cat-dog-photos', sample 10 examples from each, and exportData of the most recent annotated dataset to gs://acme-ml/labeled/v3/
AI Agent Labeling Operator
An AI agent can drive the full label-train-evaluate loop through Jentic without operator intervention. Jentic search returns the right Data Labeling operation by intent, the agent loads the schema, and Jentic executes against datalabeling.googleapis.com using vault-stored credentials. This collapses a multi-day setup into a single agent run.
Use Jentic to search 'request an image labeling job', load the labelImage schema, and execute it for dataset 'cat-dog-photos' with classification config and basic-config containing the instruction URI
Key Endpoints
28 endpoints — the google cloud data labeling api exposes the public surface of the data labeling service for ai training data preparation.
METHOD
PATH
DESCRIPTION
POST
/v1beta1/{+parent}/datasets
Create a labeling dataset
POST
/v1beta1/{+name}:importData
Import raw data from Cloud Storage
POST
/v1beta1/{+parent}/annotationSpecSets
Create an annotation spec set
POST
/v1beta1/{+name}:exportData
Export an annotated dataset to Cloud Storage
POST
/v1beta1/{+name}:pause
Pause a running labeling job
POST
/v1beta1/{+name}:resume
Resume a paused labeling job
POST
/v1beta1/{+parent}/datasets
Create a labeling dataset
POST
/v1beta1/{+name}:importData
Import raw data from Cloud Storage
POST
/v1beta1/{+parent}/annotationSpecSets
Create an annotation spec set
POST
/v1beta1/{+name}:exportData
Export an annotated dataset to Cloud Storage
POST
/v1beta1/{+name}:pause
Pause a running labeling job
POST
/v1beta1/{+name}:resume
Resume a paused labeling job
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Data Labeling API by hand means setting up Google OAuth, refreshing short-lived scoped tokens rather than holding client secrets in code, and addressing dataset and job resources on datalabeling.googleapis.com. Through Jentic you install once, import the Data Labeling API from the API Directory, store the Google credential once, and your agent calls it.
Permission scoping
The API carries the parent and resource name in the URL path (/v1beta1/{parent}/datasets and /v1beta1/{name}:importData), so a rule can pin your agent to one dataset or job. You choose the operations it may call, so it can import data and pause or resume a labeling job while export is not included unless you add it.
Credential isolation
Your Google OAuth credential 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 'submit an image labeling job', and Jentic returns the matching Data Labeling operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Data Labeling API through Jentic.
What authentication does the Data Labeling API use?
The Data Labeling API uses OAuth 2.0 with the cloud-platform scope. Through Jentic the OAuth client and refresh tokens are stored in your Jentic One instance and the agent receives short-lived scoped tokens, so raw Google credentials never enter the agent context.
Can I label images, video, and text with the Data Labeling API?
Yes. The API exposes labelImage, labelVideo, and labelText operations under the dataset resource, each accepting a labeling configuration (classification, bounding box, segmentation, entity extraction, sentiment) plus the annotation spec set and instruction URI. All three submit jobs to the same managed annotation workforce.
What are the rate limits for the Data Labeling API?
Google enforces standard Cloud quotas: per-project rate limits on read/write calls and a quota on concurrent labeling jobs per project. Quotas are visible in the Cloud Console under IAM and admin, quotas, filtered to datalabeling.googleapis.com.
How do I export a labeled dataset through Jentic?
Search Jentic for 'export an annotated dataset', load the schema for POST /v1beta1/{+name}:exportData, and execute with the AnnotatedDataset resource name and an outputConfig pointing at a Cloud Storage URI. Jentic returns the long-running operation name to poll.
Is the Data Labeling API free?
The API itself is free; Google charges per-unit fees for human-labeled examples (per image, per video minute, per text segment), with separate tier pricing for tier-1 versus tier-2 labelers. Cloud Storage costs apply to imported and exported data.
How do I pause a labeling job to fix instructions?
Call POST /v1beta1/{+name}:pause on the long-running operation associated with the labeling job. Update the Instruction document, then call POST /v1beta1/{+name}:resume to continue. Cancelled jobs cannot be resumed; only paused jobs can.
Can I limit what my agent is allowed to do with the Data Labeling API?
Yes. Because you run Jentic One yourself, your own rules decide which Data Labeling operations and credentials the agent may use. Since the API carries the parent and resource name in the URL path, such as /v1beta1/{parent}/datasets and /v1beta1/{name}:importData, you can pin the agent to a single dataset or labeling job. You also choose the exact operations it may call, so it can import data and pause or resume a job while exportData stays off unless you add it.
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