Encord AI Data Platform API
66
AI ReadinessAI-Aware (B)66/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey8 EndpointsREST
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For Agents
List and create Encord labeling projects, manage datasets, and retrieve labels and ontologies for AI training data pipelines.
Use for: I need to create a new Encord labeling project for a vision dataset, List all datasets currently in my Encord workspace, Retrieve the labels exported from a completed annotation project, Get the ontology definition for a specific labeling project
Not supported: Does not handle model training, inference serving, or data storage - use for managing Encord labeling projects, datasets, labels, and ontologies only.
Jentic publishes the only available OpenAPI specification for Encord AI Data Platform API, keeping it validated and agent-ready. Encord provides programmatic access to AI training data infrastructure, including labeling projects, datasets, ontologies, and label exports. Agents can list and create projects, provision new datasets, and pull labels for downstream model training pipelines. The API targets ML teams that need to coordinate annotation workflows and version their training label corpora.
Install Jentic One Beta
Connect the Encord AI Data Platform API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Encord AI Data Platform 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%2Fencord.com%2Fencord" | 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%2Fencord.com%2Fencord" | 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 Encord AI Data Platform API.
Create new labeling projects and inspect their configuration
List and provision datasets that hold raw annotation source data
Retrieve labels exported from completed annotation tasks
Inspect ontologies that define label classes and attributes
Look up an individual project or dataset by ID for status checks
Use Cases
Patterns agents use Encord AI Data Platform API for, with concrete tasks.
★ Bootstrapping a Labeling Project
Spin up a new annotation project in Encord by creating a dataset and a project that points to it, then retrieving the ontology so labelers know which classes to apply. The API exposes POST /projects and POST /datasets so an agent can provision a labeling workspace without clicking through the UI. This is useful when ML teams need to launch annotation runs on demand for new data drops.
Create a dataset called 'street-scenes-2026', then create a project linked to it and return the new project_id
Pulling Labels for Model Training
Export labels from completed annotation projects via GET /labels and feed them into a training pipeline. This avoids manual JSON downloads and lets agents close the loop between annotation and model retraining. Teams that retrain weekly can schedule label pulls programmatically.
Fetch all labels for project_id 'abc-123' and write them to a JSONL file ready for training
Inventorying Datasets and Projects
Run GET /projects and GET /datasets to enumerate the labeling workspace, surface stale projects, and reconcile what exists in Encord against an internal registry. Useful for ML platform teams that need a single source of truth across many annotation efforts.
List every project and dataset, then report which datasets have no associated project
Agent-Driven Annotation Orchestration
An AI agent reads a request like 'set up a new pedestrian-detection labeling run', searches Jentic for the relevant Encord operations, and chains create-dataset, create-project, and ontology lookup calls. Jentic supplies validated input schemas so the agent calls the right endpoints without browsing docs.
On request 'start labeling for batch 14', create the dataset, create a project linked to ontology_id 'ped-v3', and return both IDs
Key Endpoints
8 endpoints — jentic publishes the only available openapi specification for encord ai data platform api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
GET
/projects
List labeling projects
POST
/projects
Create a labeling project
GET
/projects/{project_id}
Get a project by ID
GET
/datasets
List datasets
POST
/datasets
Create a dataset
GET
/labels
Retrieve exported labels
GET
/ontologies
List ontologies
GET
/projects
List labeling projects
POST
/projects
Create a labeling project
GET
/projects/{project_id}
Get a project by ID
GET
/datasets
List datasets
POST
/datasets
Create a dataset
GET
/labels
Retrieve exported labels
GET
/ontologies
List ontologies
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
E
Encord AI Data Platform API
- AI-Aware (B)66/100
85
Foundational Compliance63
Developer Experience & Jentic Compatibility50
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
85
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: ASignals: 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).
42%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
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.
50
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: C-Signals: 4
44%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
57%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
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.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Encord AI Data Platform API by hand means setting up its API key in the Authorization header, formatting project, dataset, label, and ontology calls, and handling your own retries against https://api.encord.com/v2. Through Jentic you install once, import the Encord AI Data Platform API from the API Directory, store the key once, and your agent calls it.
Permission scoping
The Encord AI Data Platform API puts the project id in the URL path (/projects/{project_id}), so a rule can pin your agent to one labeling project: it can read that project and nothing else. You choose the operations it may call, so ones like creating projects or datasets are not included unless you add them.
Credential isolation
Your Encord 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 'list labeling projects' or 'read a dataset', and Jentic returns the matching Encord 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 Encord AI Data Platform API through Jentic.
Why is there no official OpenAPI spec for Encord AI Data Platform API?
Encord does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Encord AI Data Platform API via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Encord AI Data Platform API use?
The API uses API key authentication via an `apiKey` security scheme. Through Jentic, the key is stored in the encrypted credential vault and never enters the agent's prompt context - agents receive scoped access at execution time only.
Can I create new labeling projects with the Encord API?
Yes. Call POST /projects to create a project and POST /datasets to provision the underlying dataset. The 8-endpoint surface covers project, dataset, label, and ontology operations needed to bootstrap annotation work.
What are the rate limits for the Encord AI Data Platform API?
The OpenAPI spec does not declare rate limits. Treat the public endpoints as rate-limited per workspace and back off on HTTP 429 responses. Confirm exact thresholds against your Encord plan documentation.
How do I retrieve labels through Jentic?
Run `pip install jentic`, then search 'fetch encord labels for training' against Jentic. The platform returns the GET /labels operation with its input schema so the agent can supply the project filter and execute against base URL https://api.encord.com/v2.
Is the Encord AI Data Platform API free?
Encord is a paid platform. API access is included with paid Encord workspaces; pricing depends on your plan. Jentic itself does not add platform fees on top of vendor API costs.
Can I limit what my agent is allowed to do with the Encord AI Data Platform API?
Yes. Because you run Jentic One yourself, your own rules decide which Encord operations and credentials the agent can use. Since the project id sits in the URL path (/projects/{project_id}), you can pin the agent to a single labeling project so it reads that project and nothing else. You also choose the operation set, so write actions like POST /projects or POST /datasets stay off limits unless you explicitly grant them, leaving read-only calls such as GET /projects, GET /datasets, GET /labels, and GET /ontologies.
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