AWS Amazon Rekognition
Browse all Amazonaws APIs61
AI ReadinessAI-Aware (B-)61/100
Agent-ready OpenAPI document · curated by JenticAI/MLVisionhmac65 EndpointsREST
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For Agents
Detect labels, faces, text, celebrities, and unsafe content in images and video, and search face collections through Amazon Rekognition.
Use for: I need to detect objects and scenes in an uploaded image, Compare a selfie against a reference photo for identity verification, Search a face collection for the closest match to a probe image, Extract text from a scanned receipt image
Not supported: Does not generate or edit images, run text-only NLP, or transcribe audio - use for image and video analysis (labels, faces, text, moderation, celebrities, PPE) only.
Jentic publishes the only available OpenAPI specification for Amazon Rekognition, keeping it validated and agent-ready. Rekognition is a managed computer-vision service for image and video understanding: face detection, comparison, and search; object, scene, and label detection; text-in-image (OCR); content moderation; celebrity recognition; PPE detection; and stream-based video analysis. The API surfaces 65 operations across synchronous image analysis, asynchronous video jobs, face-collection management, custom Rekognition Custom Labels project lifecycle, and live-stream processors. It is the canonical AWS API for adding vision intelligence to applications.
Install Jentic One Beta
Connect the Amazon Rekognition to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Rekognition, 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%2Famazonaws.com%2Frekognition" | 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%2Famazonaws.com%2Frekognition" | 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 Amazon Rekognition API.
Detect labels, scenes, and objects in an image with bounding boxes and confidence via DetectLabels
Compare two faces and return a similarity score using CompareFaces for identity verification
Search a face collection for matches against a probe image with SearchFacesByImage
Run text-in-image OCR with DetectText, returning words, lines, and bounding geometry
Detect unsafe or moderated content with DetectModerationLabels for trust-and-safety pipelines
Start asynchronous video analysis jobs (StartLabelDetection, StartFaceSearch, StartContentModeration) and pull results when SNS signals completion
Use Cases
Patterns agents use Amazon Rekognition API for, with concrete tasks.
★ Identity Verification with Face Match
Verify that a selfie matches a stored ID photo by calling CompareFaces with both image bytes (or S3 object references) and reading the returned Similarity score. Pair with CreateFaceLivenessSession and GetFaceLivenessSessionResults to defend against spoofing using printed photos or replay video. Used in onboarding flows for fintech, ride-share, and marketplaces.
Call CompareFaces with SourceImage of the selfie bytes and TargetImage of the stored ID photo, then return the FaceMatch with the highest Similarity
Image Content Moderation
Filter user-generated content by calling DetectModerationLabels on each upload. Rekognition returns a hierarchy of moderation labels (e.g., 'Suggestive' > 'Female Swimwear or Underwear') with confidence scores so policy engines can block, age-gate, or human-review accordingly. Combine with DetectLabels and DetectText for richer signals.
Call DetectModerationLabels with Image.S3Object pointing to the uploaded image and MinConfidence 80, then route based on returned ModerationLabels
Video Label Detection at Scale
Tag long-form video assets with timestamped labels by starting an async job. StartLabelDetection takes an S3 video object and an SNS topic; when the job completes, GetLabelDetection returns labels with timestamps for every detected object or scene. Useful for media archives, ad insertion, and content discovery.
Call StartLabelDetection with Video.S3Object pointing to the source video and NotificationChannel.SNSTopicArn, then call GetLabelDetection with the returned JobId once the SNS message arrives
AI Agent Vision Tool via Jentic
A multimodal agent uses Jentic to discover Rekognition operations such as DetectLabels and DetectText, load their schemas, and call them when a tool route requires image understanding. Jentic stores AWS credentials in its vault and signs each call with SigV4, so the agent only handles the image bytes (or S3 reference) and the structured response.
Use Jentic to search 'detect labels in an image with rekognition', load DetectLabels, and execute it with the user's image bytes
Key Endpoints
65 endpoints — jentic publishes the only available openapi specification for amazon rekognition, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/#X-Amz-Target=RekognitionService.DetectLabels
Detect labels, scenes, and objects in an image
POST
/#X-Amz-Target=RekognitionService.CompareFaces
Compare two faces and return a similarity score
POST
/#X-Amz-Target=RekognitionService.DetectText
Run OCR and extract words and lines from an image
POST
/#X-Amz-Target=RekognitionService.DetectModerationLabels
Identify unsafe or moderated content categories in an image
POST
/#X-Amz-Target=RekognitionService.IndexFaces
Add faces from an image to a face collection
POST
/#X-Amz-Target=RekognitionService.SearchFacesByImage
Search a face collection for matches to a probe image
POST
/#X-Amz-Target=RekognitionService.StartLabelDetection
Start an asynchronous video label-detection job
POST
/#X-Amz-Target=RekognitionService.GetLabelDetection
Retrieve completed video label-detection results by JobId
POST
/#X-Amz-Target=RekognitionService.DetectLabels
Detect labels, scenes, and objects in an image
POST
/#X-Amz-Target=RekognitionService.CompareFaces
Compare two faces and return a similarity score
POST
/#X-Amz-Target=RekognitionService.DetectText
Run OCR and extract words and lines from an image
POST
/#X-Amz-Target=RekognitionService.DetectModerationLabels
Identify unsafe or moderated content categories in an image
POST
/#X-Amz-Target=RekognitionService.IndexFaces
Add faces from an image to a face collection
POST
/#X-Amz-Target=RekognitionService.SearchFacesByImage
Search a face collection for matches to a probe image
POST
/#X-Amz-Target=RekognitionService.StartLabelDetection
Start an asynchronous video label-detection job
POST
/#X-Amz-Target=RekognitionService.GetLabelDetection
Retrieve completed video label-detection results by JobId
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.
A
Amazon Rekognition
- AI-Aware (B-)61/100
99
Foundational Compliance63
Developer Experience & Jentic Compatibility38
AI-Readiness & Agent Experience92
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
99
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
94%
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).
100%
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
1%
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.
38
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
51%
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.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
92
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
92%
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 Rekognition by hand means computing SigV4 HMAC signatures, setting the X-Amz-Target header per action, routing to the regional rekognition host, handling S3 references for image and video inputs, and polling asynchronous video jobs yourself. Through Jentic you install once, import Amazon Rekognition from the API Directory, store your AWS access keys once, and your agent calls it with signing and targeting handled per request.
Permission scoping
The image, video, and collection targets travel in the request body rather than the URL path, so you limit the agent to the operations it needs, such as DetectLabels, DetectText, and DetectModerationLabels for analysis. Operations that mutate a face collection, like IndexFaces or deleting a collection, are included only when you grant them.
Credential isolation
Your AWS access keys are stored once, encrypted, by your own Jentic One instance and used to produce a SigV4 signature per request. The raw secret access key never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'detect labels in an image', and Jentic returns DetectLabels with its input schema for Image, MinConfidence, and Features 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 Amazon Rekognition API through Jentic.
Why is there no official OpenAPI spec for Amazon Rekognition?
AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Rekognition 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 Rekognition API use?
Rekognition uses AWS SigV4 HMAC request signing with an AWS access key ID and secret. Through Jentic, AWS credentials are stored in the vault and SigV4 signatures are produced per request, so the agent never sees raw secrets.
Can I run Rekognition on video as well as images?
Yes. Synchronous image operations (DetectLabels, DetectFaces, DetectText, etc.) accept Image bytes or an S3 object. Video operations are asynchronous: call StartLabelDetection, StartFaceSearch, StartContentModeration, or StartCelebrityRecognition with a Video.S3Object and NotificationChannel, then call the matching Get* operation with the returned JobId.
What image formats and sizes are supported?
Rekognition accepts JPEG and PNG. Synchronous calls cap image bytes at 5 MB when passed inline and 15 MB when referenced via S3. Minimum face size for detection is roughly 40x40 pixels. Videos must be H.264 in MP4 or MOV containers and live in S3.
What are the rate limits for Rekognition?
Rekognition enforces per-region transactions-per-second limits per operation; defaults vary (DetectLabels often starts around 50 TPS) and quota increases can be requested. Throttled calls return ProvisionedThroughputExceededException - back off and retry.
How do I detect labels in an image through Jentic?
Search Jentic for 'detect labels in an image with rekognition', load DetectLabels, and execute it with Image bytes (or Image.S3Object) and MinConfidence. Install with pip install jentic; AWS credentials are pulled from your Jentic One instance.
Can I limit what my agent is allowed to do with the Amazon Rekognition API?
Yes. Because you run Jentic One yourself, your own rules decide which Rekognition operations and which AWS credentials the agent may use. You can grant read-only analysis calls such as DetectLabels, DetectText, and DetectModerationLabels while withholding operations that mutate a face collection, like IndexFaces or deleting a collection. The image, video, and collection targets travel in the request body rather than the URL path, so the operator scopes access at the operation level and the agent can only call what you allow.
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