Agent-ready OpenAPI document · curated by JenticAI/MLVisionapiKey14 EndpointsREST
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For Agents
Run AI predictions against Clarifai's pre-trained or custom vision and language models, manage the inputs and concepts in an app, and execute multi-step workflows. Useful for any agent that needs vision tagging, embedding generation, or content moderation.
Use for: I need to detect objects in this product photo with a Clarifai model, Classify a batch of moderation candidates against the NSFW model, Search our app's input index for images similar to a sample upload, Run our chained tagging workflow on a new asset
Not supported: Does not handle pixel-level image editing, video transcoding, or model training pipelines - use for prediction, input management, semantic search, and workflow execution against Clarifai-hosted models only.
Jentic publishes the only available OpenAPI specification for this Clarifai API variant, keeping it validated and agent-ready. Clarifai is a computer vision and NLP platform that runs pre-trained and custom AI models for image recognition, object detection, face detection, text classification, and embedding generation. This spec covers the v2 prediction surface - model inference, input management, semantic search, workflow execution, and concept management - scoped under a user_id and app_id pair so each request is namespaced to the right application. Authentication uses a Personal Access Token in the Authorization header.
Install Jentic One Beta
Connect the Clarifai API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Clarifai 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%2Fclarifai.com%2Fclarifai-clarifai" | 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%2Fclarifai.com%2Fclarifai-clarifai" | 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 Clarifai API.
Run a prediction against a Clarifai model and receive concept tags, bounding boxes, or embeddings
Pin a prediction to a specific model version for reproducible outputs
Add, list, and remove inputs (images, video, text) within a Clarifai app
Run semantic search over indexed inputs to find visually or conceptually similar items
Execute a multi-model workflow against a single input in one call
Manage the concept taxonomy of an app - list and add concepts used as labels
Use Cases
Patterns agents use Clarifai API for, with concrete tasks.
★ Image moderation pipeline for user-generated content
A marketplace runs every uploaded photo through a Clarifai moderation model by calling POST /users/{user_id}/apps/{app_id}/models/{model_id}/outputs with the image URL. Predictions returning unsafe concepts are routed to a human review queue, while clean inputs are added to the app's index via POST /users/{user_id}/apps/{app_id}/inputs for later semantic search.
POST a moderation prediction for a new upload, and if any unsafe concept exceeds 0.8, queue the asset for review
Visual search for product catalogues
An ecommerce team indexes the product catalogue as Clarifai inputs, then exposes a 'shop the look' feature backed by POST /users/{user_id}/apps/{app_id}/searches. The search request takes a sample image and returns visually similar items from the indexed corpus. Pinning predictions to a specific model version keeps embeddings consistent across reindexes.
Index 1,000 product images as Clarifai inputs, then search the index with a customer-uploaded photo and return top 10 matches
Custom workflow for multi-step tagging
Teams that need more than a single model output build a Clarifai workflow that chains general tagging, brand detection, and demographic estimation, then run it with POST /users/{user_id}/apps/{app_id}/workflows/{workflow_id}/results. One API call returns the combined output, which simplifies the calling agent's parsing and reduces network round-trips.
Run a workflow that tags an image, detects brand logos, and estimates demographics, then return the merged output
Agent integration via Jentic
Through Jentic, an agent can resolve intents like 'tag this image with general concepts' to the right Clarifai prediction endpoint, including the user_id, app_id, and model_id path parameters that scope the request. The PAT is stored in the Jentic credential vault and only injected at execution time.
Search Jentic for 'predict concepts in an image', load the schema for the prediction endpoint, and execute it with the supplied image URL
Key Endpoints
14 endpoints — jentic publishes the only available openapi specification for this clarifai api variant, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/users/{user_id}/apps/{app_id}/models/{model_id}/outputs
Run a prediction against a model
POST
/users/{user_id}/apps/{app_id}/inputs
Add inputs to the app
GET
/users/{user_id}/apps/{app_id}/inputs
List inputs
POST
/users/{user_id}/apps/{app_id}/searches
Search inputs
GET
/users/{user_id}/apps/{app_id}/models
List models
POST
/users/{user_id}/apps/{app_id}/workflows/{workflow_id}/results
Run a workflow
GET
/users/{user_id}/apps/{app_id}/concepts
List concepts
POST
/users/{user_id}/apps/{app_id}/concepts
Add concepts
POST
/users/{user_id}/apps/{app_id}/models/{model_id}/outputs
Run a prediction against a model
POST
/users/{user_id}/apps/{app_id}/inputs
Add inputs to the app
GET
/users/{user_id}/apps/{app_id}/inputs
List inputs
POST
/users/{user_id}/apps/{app_id}/searches
Search inputs
GET
/users/{user_id}/apps/{app_id}/models
List models
POST
/users/{user_id}/apps/{app_id}/workflows/{workflow_id}/results
Run a workflow
GET
/users/{user_id}/apps/{app_id}/concepts
List concepts
POST
/users/{user_id}/apps/{app_id}/concepts
Add concepts
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.
C
Clarifai API
- AI-Aware (B)67/100
100
Foundational Compliance69
Developer Experience & Jentic Compatibility46
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
100
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
98%
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.
69
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.
75%
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.
46
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: DSignals: 4
25%
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.
58%
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 Clarifai API by hand means managing its API key and threading predictions, input management, search, and workflow runs through user- and app-scoped endpoints yourself. Through Jentic you install once, import the Clarifai API from the API Directory, store the API key once, and your agent calls it.
Permission scoping
Clarifai puts the user and app id in the URL path (/users/{user_id}/apps/{app_id}/...), so a rule can pin your agent to one app for prediction and search. You choose the operations it may call, so adding concepts or running workflows is not included unless you add it.
Credential isolation
Your Clarifai 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 'predict concepts for an image' or 'search inputs in an app', and Jentic returns the matching Clarifai 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 Clarifai API through Jentic.
Why is there no official OpenAPI spec for this Clarifai API variant?
Clarifai publishes a gRPC-first reference and language SDKs rather than a maintained OpenAPI specification for the REST surface. Jentic generates and maintains this spec from the public REST documentation so AI agents and developers can call Clarifai through 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 Clarifai API use?
Clarifai uses a Personal Access Token sent in the Authorization header with the format 'Key YOUR_PAT_HERE'. PATs are issued from https://clarifai.com/settings/security. Through Jentic, the PAT is stored encrypted in the credential vault and injected at execution time.
Can I run predictions against my own custom Clarifai models?
Yes. The prediction endpoint at /users/{user_id}/apps/{app_id}/models/{model_id}/outputs takes whichever model_id you target, including custom models trained in your app. To pin output stability, point to a specific version with /models/{model_id}/versions/{model_version_id}/outputs.
How do I run a Clarifai prediction through Jentic?
Search Jentic for 'predict concepts in an image'. Jentic returns POST /users/{user_id}/apps/{app_id}/models/{model_id}/outputs with its input schema. Load the schema, supply user_id, app_id, model_id, and the image data, and execute - the SDK handles the PAT header for you.
What is the difference between this spec and the clarifai-api spec under the same vendor?
Both specs cover the Clarifai REST surface; this 'clarifai' spec uses a base URL with the /v2 prefix already applied and slightly different path parameter casing (user_id vs userId). Pick whichever matches the codebase you are integrating from - both are kept in the catalogue for compatibility with existing integrations.
Does the search endpoint do semantic similarity?
Yes. POST /users/{user_id}/apps/{app_id}/searches performs semantic search over inputs that have been indexed in the app, returning items ranked by similarity to the query input. It is the standard primitive for visual search, deduplication, and 'similar items' features.
Can I limit what my agent is allowed to do with the Clarifai API?
Yes. Because you run Jentic One yourself, your own rules decide which Clarifai operations and credentials the agent can use. Clarifai puts the user and app id in the URL path (/users/{user_id}/apps/{app_id}/...), so you can pin the agent to a single app and allow only read-style calls like running predictions and searching indexed inputs. Write operations such as adding concepts or running workflows stay off limits unless you explicitly grant them, and your stored API key is injected only at execution time.
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