Clarifai API
Browse all Clarifai APIs★ Only Publicly Available OpenAPI DocumentAI/MLVisionapiKey14 EndpointsREST
For Agents
Run AI predictions against Clarifai vision and NLP models, manage inputs and concepts within an app, and execute multi-model workflows. Useful for any agent that needs image tagging, embeddings, or content moderation.
Use for: I need to classify a set of images against the general model, Detect objects in this video frame and return bounding boxes, Search our Clarifai app for inputs similar to this sample, Run our custom moderation workflow on a new upload
Not supported: Does not handle pixel-level image editing, video transcoding, or large-scale model training pipelines — use for prediction, input management, semantic search, workflow execution, and concept management 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 offering pre-trained and custom models for image classification, object detection, text analysis, and embedding generation. This spec covers the v2 REST surface with the /v2 prefix on every path and camelCase path parameters (userId, appId, modelId), making it convenient for clients generated from this OpenAPI document. Authentication uses a Personal Access Token or API Key sent as 'Key {value}' 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.
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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 | sh2
Step 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.
Capabilities
What an agent can do with Clarifai API.
Run a prediction against a Clarifai model and receive concept tags, regions, or embeddings
Pin a prediction to a specific model version for reproducible outputs
Create, list, get, and delete custom models within a Clarifai app
Add and manage inputs (images, video, text) used for indexing or training
Run semantic search over indexed inputs to find similar items
Execute a multi-model workflow against a single input in one call
Manage the concept taxonomy used to label inputs and predictions
Use Cases
Patterns agents use Clarifai API for, with concrete tasks.
★ Custom model lifecycle for visual quality inspection
A manufacturer trains a custom Clarifai model on examples of acceptable and defective parts, then calls POST /v2/users/{userId}/apps/{appId}/models to register the model and POST /v2/users/{userId}/apps/{appId}/models/{modelId}/outputs to run inference on each part photographed on the line. Versions can be deleted via DELETE on the model id when retired.
Create a custom model with the 'good' and 'defective' concepts, then run prediction on a new line photo and return the top concept
Content tagging and embedding for a media library
A media team uploads assets via POST /v2/users/{userId}/apps/{appId}/inputs and runs the general embedding model to tag each one. The embeddings power downstream semantic search through POST /v2/users/{userId}/apps/{appId}/searches. The team can list inputs and remove ones that should not be indexed via the matching DELETE endpoint.
Add 200 media assets as inputs, run general embeddings, and search for the top 5 nearest neighbours of a sample asset
Concept taxonomy management
Teams maintain a concept taxonomy that drives both labelling and prediction outputs. POST /v2/users/{userId}/apps/{appId}/concepts adds new concepts, GET lists the current taxonomy. Keeping the taxonomy explicit lets the application code rely on a stable label vocabulary rather than free-text tags.
Add three new concepts ('outdoor-furniture', 'indoor-furniture', 'lighting') to the app and verify they appear in GET /v2/users/{userId}/apps/{appId}/concepts
Agent integration via Jentic
Through Jentic, an agent can resolve intents like 'classify this image' or 'find similar inputs' to the right Clarifai endpoint, including the userId, appId, and modelId path parameters. The PAT or API key lives in the Jentic credential vault and is attached to the Authorization header only at execution time.
Search Jentic for 'run a Clarifai prediction', load the schema for POST /v2/users/{userId}/apps/{appId}/models/{modelId}/outputs, and execute it with the supplied image
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
/v2/users/{userId}/apps/{appId}/models/{modelId}/outputs
Run a prediction
POST
/v2/users/{userId}/apps/{appId}/models
Create a model
GET
/v2/users/{userId}/apps/{appId}/models
List models
POST
/v2/users/{userId}/apps/{appId}/inputs
Add inputs
GET
/v2/users/{userId}/apps/{appId}/inputs
List inputs
POST
/v2/users/{userId}/apps/{appId}/searches
Search inputs
POST
/v2/users/{userId}/apps/{appId}/workflows/{workflowId}/results
Run a workflow
POST
/v2/users/{userId}/apps/{appId}/concepts
Create concepts
POST
/v2/users/{userId}/apps/{appId}/models/{modelId}/outputs
Run a prediction
POST
/v2/users/{userId}/apps/{appId}/models
Create a model
GET
/v2/users/{userId}/apps/{appId}/models
List models
POST
/v2/users/{userId}/apps/{appId}/inputs
Add inputs
GET
/v2/users/{userId}/apps/{appId}/inputs
List inputs
POST
/v2/users/{userId}/apps/{appId}/searches
Search inputs
POST
/v2/users/{userId}/apps/{appId}/workflows/{workflowId}/results
Run a workflow
POST
/v2/users/{userId}/apps/{appId}/concepts
Create concepts
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Clarifai PATs and API keys are stored encrypted in the Jentic vault and added to the Authorization header at execution time using the required 'Key {value}' format. The raw credential never enters the agent's prompt or logs.
Intent-based discovery
Agents search Jentic with intents like 'classify an image with Clarifai' or 'add inputs to a Clarifai app'. Jentic returns the matching v2 endpoint with its input schema, including the userId and appId placeholders.
Time to first call
Direct integration: 1-2 days to learn the userId/appId/modelId path scoping and the 'Key {value}' header format. Through Jentic: under 1 hour — search, load schema, execute.
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 the Clarifai API?
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 at https://app.jentic.com/sign-up.
What authentication does the Clarifai API use?
Clarifai uses a Personal Access Token or API Key sent in the Authorization header in the format 'Key {your_pat_or_api_key}'. Through Jentic, the credential is stored encrypted in the credential vault and attached to outgoing requests only at execution time.
Can I create custom models with this API?
Yes. POST /v2/users/{userId}/apps/{appId}/models registers a new model, and predictions against it are issued at /v2/users/{userId}/apps/{appId}/models/{modelId}/outputs. Versions of the model are addressable via the /versions/{versionId}/outputs sub-path for reproducibility.
How do I run a workflow through Jentic?
Search Jentic for 'run a Clarifai workflow'. Jentic returns POST /v2/users/{userId}/apps/{appId}/workflows/{workflowId}/results with its input schema. Load the schema, supply userId, appId, workflowId, and the input data, and execute — the SDK handles the 'Key {pat}' Authorization header for you.
What is the difference between this spec and the 'clarifai' spec under the same vendor?
Both specs cover the Clarifai REST surface. This 'clarifai-api' variant uses a root base URL and includes /v2 in every path, with camelCase path parameters (userId, appId). The sibling 'clarifai' spec bakes /v2 into the base URL and uses snake_case parameters. Pick whichever matches the casing your codebase already uses.
Does this API support semantic search across my own inputs?
Yes. POST /v2/users/{userId}/apps/{appId}/searches accepts a query input and returns indexed inputs ranked by similarity. This is the standard primitive for visual search, near-duplicate detection, and 'find more like this' features once your inputs are indexed.
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