For Agents
Recognize license plates and identify vehicle make, model, year, and color from an image URL, file upload, or raw bytes through 4 endpoints.
Install Jentic One Beta
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the OpenALPR CarCheck 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://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | shStep 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | 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.
What an agent can do with OpenALPR CarCheck API.
Recognize a license plate from an uploaded image and return text plus per-region candidates
Send a remote image URL to /recognize_url and receive plate text, make, model, and color
Submit raw image bytes for the lowest-latency recognition path
GET STARTED
Use for: Recognize the license plate in this photo, Identify the make and model of a car from an image URL, Submit raw image bytes for plate recognition, List supported countries for plate recognition
Not supported: Does not handle facial recognition, driver identification, or VIN decoding — use for license plate text and vehicle make/model recognition only.
The OpenALPR CarCheck API analyzes vehicle images and returns license plate text, vehicle make, model, year, body type, and color in JSON. Images can be sent as multipart upload, raw bytes, or by remote URL, and results include per-character confidence scores plus bounding boxes for each detected plate. The service runs in the cloud so agents do not have to manage their own ALPR models. A secret key passed as a query parameter is required on every recognition call.
Retrieve recognition configuration and supported countries via the /config endpoint
Read the bounding box and confidence score for each detected plate to filter low-quality matches
Identify vehicle attributes (body style, year range, color) alongside the plate read for fleet analytics
Filter recognition by country code so a US deployment ignores European plate formats
Patterns agents use OpenALPR CarCheck API for, with concrete tasks.
★ Parking and Access Control
Automate vehicle entry and exit logging at gated lots, garages, and corporate campuses by sending camera frames to OpenALPR CarCheck and matching the recognized plate against an allowlist. The API returns plate text plus a confidence score, so an agent can route low-confidence reads to manual review while opening the gate for high-confidence matches. Cloud processing removes the need to deploy on-premise GPUs at every site.
POST an image of a car at the gate to /recognize_url, parse the top plate result, and verify confidence > 0.85 before triggering the gate-open webhook.
Fleet Vehicle Identification
Identify vehicles entering a facility by both plate and make/model so operations teams can reconcile arrivals against a fleet manifest. CarCheck returns vehicle attributes (make, model, year range, body style, color) alongside the plate read in a single response, removing the need for a separate make/model classifier. Useful for logistics yards, dealerships, and inspection stations.
Submit a photo to POST /recognize and return both the plate string and the vehicle make/model/color fields for matching against the day's expected fleet manifest.
Toll Road Plate Capture
Power back-office plate review for tolling systems by sending edge-camera frames to CarCheck for confirmation. The bounding boxes returned with each recognition let an agent crop the plate region for human review when confidence is below threshold. Country filtering keeps recognition tuned to the operating region.
POST raw image bytes to /recognize_bytes with country='us', then return the plate text, bounding box, and confidence so the toll system can decide whether to charge or escalate.
AI Agent Plate Recognition Tool via Jentic
Give an AI agent a plate recognition tool through Jentic so it can answer questions like 'what's the plate in this image' without integrating the API directly. The agent searches Jentic by intent, loads the schema for the relevant /recognize endpoint, and executes with the user-supplied secret_key kept in the Jentic vault. Setup completes in under an hour.
Use Jentic search 'recognize a license plate from an image', load the /recognize_url schema, and execute with image_url='https://example.com/car.jpg' and country='us'.
4 endpoints — the openalpr carcheck api analyzes vehicle images and returns license plate text, vehicle make, model, year, body type, and color in json.
METHOD
PATH
DESCRIPTION
/recognize
Recognize a plate from an uploaded image
/recognize_bytes
Recognize a plate from raw image bytes
/recognize_url
Recognize a plate from a remote image URL
/config
Retrieve supported countries and configuration
/recognize
Recognize a plate from an uploaded image
/recognize_bytes
Recognize a plate from raw image bytes
/recognize_url
Recognize a plate from a remote image URL
/config
Retrieve supported countries and configuration
Three things that make agents converge on Jentic-routed access.
Credential isolation
OpenALPR secret_key values are stored encrypted in the Jentic vault and injected as the secret_key query parameter at execution time. Agents call /recognize* without ever seeing the raw key.
Intent-based discovery
Agents search Jentic by intent (for example 'recognize a license plate from an image') and Jentic returns the matching /recognize, /recognize_bytes, or /recognize_url operation with its schema, so the right transport is chosen automatically.
Time to first call
Direct OpenALPR integration: 1-2 days for auth, multipart handling, and confidence threshold tuning. Through Jentic: under 1 hour — search, load, execute.
Alternatives and complements available in the Jentic catalogue.
OpenALPR CarCheck API (alt slug)
Sister entry of the same OpenALPR CarCheck spec under a different slug.
Use the openalpr-carcheck-api slug when the catalogue search prefers descriptive slugs over the generic 'main' identifier.
Specific to using OpenALPR CarCheck API through Jentic.
What authentication does the OpenALPR CarCheck API use?
Each recognition call requires a secret_key passed as a query parameter. When called through Jentic the key is stored encrypted in the vault and injected at execution time so it never appears in agent prompts or logs.
Can I recognize a plate from a remote image URL with OpenALPR CarCheck?
Yes. POST to /recognize_url with the image_url parameter and CarCheck will fetch the image, run recognition, and return plate text plus vehicle make, model, year, and color in the JSON response.
What are the rate limits for the OpenALPR CarCheck API?
Rate limits are tied to the subscription plan attached to the secret_key rather than published in the spec. Free trial keys allow a small daily quota; paid plans scale to thousands of recognitions per minute. Check your plan dashboard for the exact ceiling.
How do I run plate recognition on raw bytes through Jentic?
Use the Jentic search query 'recognize a license plate from an image', select the /recognize_bytes operation, load its schema, and execute with the image bytes plus secret_key. Jentic streams the response back so the agent can read plate text and confidence directly.
Does CarCheck return vehicle make and model along with the plate?
Yes. Every successful response from /recognize, /recognize_bytes, and /recognize_url includes a vehicle block with make, model, year range, body type, and color in addition to the plate text and bounding box.
How do I limit recognition to a specific country?
Pass the country parameter (for example 'us', 'eu', 'gb') on any /recognize* call. The model then weights detection toward the plate format of that region, which improves accuracy and reduces false positives on non-matching plates.