Coming

REST API for Curation

The Live Capture API streams data in. The REST API lets you programmatically manage it. Review, rate, approve, reject — connect external tools, automation scripts, or AI agents to reduce human involvement in the curation loop.

Full-cycle automation

AI Curator isn't just a tool where a human clicks "approve" on every sample. The REST API lets you connect external tools, automation scripts, and even other AI systems to review, rate, approve, and reject datasets and samples. Reduce human involvement to the edges — humans set the rules, automation handles the volume.

StageIngestCurateExport
InterfaceLive Capture APIWeb UI / REST APICLI / REST API
AutomationFull — POST JSON, doneConfigurable — AI, rules, or humanFull — scheduled, filtered
Human roleMinimalAs much or as little as you wantMinimal

Live Capture API streams data in. REST API manages curation out. Together, they close the loop. Data comes in, gets curated with as much or as little human involvement as you want, and gets exported — all automatable.

Planned Endpoints

The REST API for curation is under active development. These endpoints represent the planned API surface. The base URL is the same as AI Curator's web UI — local or your own server.

Datasets

MethodEndpointDescription
GET/api/datasetsList all datasets
GET/api/datasets/:idGet dataset details, sample counts, status breakdown
POST/api/datasetsCreate a new dataset
DELETE/api/datasets/:idDelete a dataset
POST/api/datasets/:id/exportExport dataset with format and filter options

Samples

MethodEndpointDescription
GET/api/datasets/:id/samplesList samples with filtering (status, category, quality)
GET/api/samples/:idGet sample details
PATCH/api/samples/:idUpdate sample — change status, rating, category, tags
POST/api/samples/:id/approveApprove a sample
POST/api/samples/:id/rejectReject a sample
POST/api/datasets/:id/samples/bulkBulk update samples — approve/reject/rate by filter

Automation Patterns

The REST API enables several automation patterns — from simple rule-based filters to full AI-assisted curation pipelines.

Rule-Based Approval
Auto-approve samples above a quality threshold. Flag low-quality samples for human review only.
AI-Assisted Curation
Send samples to another LLM for pre-review. Let it rate quality, suggest categories, flag duplicates. Human spot-checks the results.
CI/CD Integration
Hook your pipeline into curation. Auto-approve above threshold, flag exceptions, export on schedule.
Compliance Workflows
Connect internal tools to enforce review policies. Integrate with ticketing and content management.

Example: Auto-approve above quality threshold

Python
 import requests BASE = "http://localhost:3333/api" # Get all pending samples in dataset 1 samples = requests.get(f"{BASE}/datasets/1/samples", params={ "status": "draft", "minQuality": 3 }).json() # Auto-approve quality 4+ with categories for sample in samples: if sample["qualityRating"] >= 4 and sample.get("category"): requests.post(f"{BASE}/samples/{sample['id']}/approve") else: # Flag for human review requests.patch(f"{BASE}/samples/{sample['id']}", json={ "status": "in_review", "tags": ["needs-review"] })

Example: AI-assisted pre-review

Python
 import requests BASE = "http://localhost:3333/api" # Get draft samples samples = requests.get(f"{BASE}/datasets/1/samples", params={ "status": "draft" }).json() for sample in samples: # Send to your LLM for pre-review review = your_llm_review(sample["instruction"], sample["output"]) requests.patch(f"{BASE}/samples/{sample['id']}", json={ "qualityRating": review["quality"], "category": review["category"], "tags": review.get("flags", []) }) # Human spot-checks "in_review" only if review["quality"] >= 4: requests.post(f"{BASE}/samples/{sample['id']}/approve")

Who is this for?

AudienceWhat they do with the REST API
ML EngineersScript review-and-approve workflows. Filter by category and quality before export. Automate the entire pipeline end-to-end.
Dev TeamsHook CI/CD into curation. Auto-approve above quality threshold. Flag low-quality for human review only.
EnterpriseConnect internal tools to the curation pipeline. Integrate with ticketing systems, content management, compliance workflows.
AI CurationSend samples to another LLM for pre-review. Rate quality, suggest categories, flag duplicates. Human spot-checks results.
Reduce human involvement to the edges
The REST API doesn't eliminate human review — it reduces it. Humans set the rules and handle edge cases. Automation handles the volume. Same curation quality, fraction of the effort.

Roadmap

The REST API for curation is under active development. The Live Capture API (data ingestion) is available now. The curation and export endpoints are coming next.

Available
Live Capture API
Stream data in from any source via POST /api/capture
Coming
Curation Endpoints
Review, rate, approve, reject — programmatically manage samples and datasets
Coming
Export Endpoints
Trigger filtered exports, schedule automated exports, integrate with training pipelines
Planned
AI Curator SDK
Embed capture, curate, and export directly in your code — Python, Node, whatever you use