Cloud platform for data-centric AI - upload datasets, run automated data curation and error detection, train AutoML models, and deploy them for real-time REST inference. Driven by the cleanlab-studio Python client and a REST inference API.
openapi: 3.0.1
info:
title: Cleanlab API
description: >-
Specification of the Cleanlab data-and-AI trust platform APIs. Covers the
Trustworthy Language Model (TLM) OpenAI-compatible Chat Completions endpoint,
the Codex / Cleanlab AI Platform project validation (guardrail and
remediation) endpoint, and the Cleanlab Studio deployed-model REST inference
endpoint. All endpoints are HTTPS REST and authenticate with a Cleanlab API
key (or project access key) supplied as a Bearer token.
termsOfService: https://cleanlab.ai/legal/terms-of-service/
contact:
name: Cleanlab Support
email: support@cleanlab.ai
version: '1.0'
servers:
- url: https://api.cleanlab.ai
description: Cleanlab platform API
paths:
/api/v1/openai_trustworthy_llm/v1/chat/completions:
post:
operationId: createTrustworthyChatCompletion
tags:
- TLM
summary: Trustworthy chat completion (OpenAI-compatible)
description: >-
OpenAI-compatible Chat Completions endpoint that returns a standard
completion plus a Cleanlab trustworthiness score. Point the OpenAI
client base_url at https://api.cleanlab.ai/api/v1/openai_trustworthy_llm/
and the client appends /v1/chat/completions automatically. The
trustworthiness score (0-1) is returned in tlm_metadata on the response.
Set stream=true to receive the completion as Server-Sent Events; the
final SSE chunk carries the tlm_metadata.
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/ChatCompletionRequest'
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/ChatCompletionResponse'
text/event-stream:
schema:
type: string
description: Server-Sent Events stream of chat completion chunks (stream=true).
'401':
description: Invalid or missing API key.
'422':
description: Invalid request parameters.
'429':
description: Rate limit exceeded.
/api/v1/codex/projects/{project_id}/validate:
post:
operationId: validateResponse
tags:
- Codex
summary: Validate an AI response (guardrail + remediation)
description: >-
Validates an AI application's response against a Codex project. Returns
whether the response should be guardrailed, eval scores across
configured criteria, whether it was escalated to a subject-matter
expert, and an expert answer to remediate the response when a
semantically similar verified answer exists in the project. Authenticate
with the project access key as a Bearer token.
parameters:
- name: project_id
in: path
required: true
description: The Cleanlab Codex project identifier.
schema:
type: string
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/ValidateRequest'
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/ValidateResponse'
'401':
description: Invalid or missing access key.
'404':
description: Project not found.
'422':
description: Invalid request parameters.
/api/v1/deployment/{model_id}/predict:
post:
operationId: predict
tags:
- Studio
summary: Predict with a deployed Cleanlab Studio model
description: >-
Runs real-time inference against a model trained and deployed in
Cleanlab Studio. Submit one or more text/tabular records and receive
predicted labels and (optionally) predicted class probabilities.
Authenticate with the Cleanlab Studio API key as a Bearer token.
parameters:
- name: model_id
in: path
required: true
description: The deployed Cleanlab Studio model identifier.
schema:
type: string
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/PredictRequest'
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/PredictResponse'
'401':
description: Invalid or missing API key.
'404':
description: Model not found.
'422':
description: Invalid request parameters.
components:
securitySchemes:
bearerAuth:
type: http
scheme: bearer
description: >-
Cleanlab API key (TLM / Studio) or Codex project access key, supplied as
a Bearer token in the Authorization header.
schemas:
ChatCompletionRequest:
type: object
required:
- model
- messages
properties:
model:
type: string
description: >-
Base LLM to wrap, e.g. gpt-4.1-mini, gpt-5.1, gpt-4.1, o3,
claude-opus-4-0, claude-sonnet-4-0, nova-pro, and most LLMs that
support the Chat Completions API.
example: gpt-4.1-mini
messages:
type: array
description: Conversation messages in OpenAI Chat Completions format.
items:
$ref: '#/components/schemas/ChatMessage'
temperature:
type: number
format: float
nullable: true
max_tokens:
type: integer
nullable: true
stream:
type: boolean
default: false
description: When true, the response is delivered as Server-Sent Events.
extra_body:
type: object
description: >-
Cleanlab-specific options, e.g. quality_preset and log. Use
"log": ["explanation"] to also return a natural-language explanation
of the trustworthiness score.
additionalProperties: true
ChatMessage:
type: object
required:
- role
- content
properties:
role:
type: string
enum:
- system
- user
- assistant
- tool
content:
type: string
ChatCompletionResponse:
type: object
properties:
id:
type: string
object:
type: string
example: chat.completion
created:
type: integer
model:
type: string
choices:
type: array
items:
$ref: '#/components/schemas/ChatCompletionChoice'
usage:
$ref: '#/components/schemas/Usage'
tlm_metadata:
$ref: '#/components/schemas/TLMMetadata'
ChatCompletionChoice:
type: object
properties:
index:
type: integer
message:
$ref: '#/components/schemas/ChatMessage'
finish_reason:
type: string
TLMMetadata:
type: object
description: Cleanlab trustworthiness metadata attached to the completion.
properties:
trustworthiness_score:
type: number
format: float
description: Trustworthiness of the response on a 0-1 scale.
example: 0.9873
log:
type: object
description: Optional extra metadata such as a trust-score explanation.
additionalProperties: true
Usage:
type: object
properties:
prompt_tokens:
type: integer
completion_tokens:
type: integer
total_tokens:
type: integer
ValidateRequest:
type: object
required:
- response
properties:
query:
type: string
description: The core user question.
context:
type: string
description: Retrieved context supplied to the LLM (for RAG).
response:
type: string
description: The AI-generated response being validated.
messages:
type: array
description: Full prompt / conversation history in Chat Completions format.
items:
$ref: '#/components/schemas/ChatMessage'
rewritten_query:
type: string
nullable: true
eval_scores:
type: object
description: Pre-computed eval scores to pass through instead of recomputing.
additionalProperties:
type: number
metadata:
type: object
additionalProperties: true
ValidateResponse:
type: object
properties:
should_guardrail:
type: boolean
description: Whether the response should be suppressed or replaced.
escalated_to_sme:
type: boolean
description: Whether the query was escalated to a subject-matter expert.
eval_scores:
type: object
description: Numeric scores across configured evaluation criteria.
additionalProperties:
type: object
additionalProperties: true
expert_answer:
type: string
nullable: true
description: A verified expert answer to remediate the response, if available.
log_id:
type: string
description: Identifier of the logged query record.
PredictRequest:
type: object
required:
- data
properties:
data:
type: array
description: Records to score (text strings or row objects).
items:
type: object
additionalProperties: true
return_pred_proba:
type: boolean
default: false
description: Whether to also return predicted class probabilities.
PredictResponse:
type: object
properties:
predictions:
type: array
description: Predicted label for each input record.
items:
type: string
pred_proba:
type: array
description: Per-class probabilities for each record (when requested).
items:
type: array
items:
type: number
format: float
security:
- bearerAuth: []