Agent-ready OpenAPI document · curated by JenticAI/MLLanguage Modelsbearer7 EndpointsREST
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For Agents
Run chat completions, text completions, embeddings, image generation, audio transcription, and text-to-speech through one OpenAI-style endpoint set, and list supported models.
Use for: Generate a chat completion for a multi-turn conversation, Create an embedding vector for a chunk of text, Generate an image from a text prompt, Transcribe a voice memo into text
Not supported: Does not handle model fine-tuning, training, or hosted vector storage - use for inference across chat, embeddings, image, and audio only.
Jentic publishes the only available OpenAPI specification for AI/ML API, keeping it validated and agent-ready. AI/ML API is a unified inference platform that exposes chat completions, completions, embeddings, image generation, audio transcription, and text-to-speech behind a single Bearer-authenticated REST surface modelled on OpenAI conventions. The /v1/models endpoint lists supported models so a client can switch backends without changing integration code.
Install Jentic One Beta
Connect the AI/ML API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the AI/ML 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%2Faimlapi.com%2Faiml-api" | 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%2Faimlapi.com%2Faiml-api" | 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 AI/ML API.
Generate chat completions for any supported model via POST /v1/chat/completions
Run plain text completions with /v1/completions for prompt-based generation
Create embedding vectors for retrieval and semantic search through /v1/embeddings
Generate images from a text prompt via /v1/images/generations
Transcribe audio to text using /v1/audio/transcriptions
Synthesize speech from text via /v1/audio/speech
Discover available models programmatically with GET /v1/models
Use Cases
Patterns agents use AI/ML API for, with concrete tasks.
★ Multi-Model Chat Backend
Power a chat product where users can switch between different LLMs without changing the application code. Call /v1/chat/completions with the desired model and use /v1/models to populate a model selector. Switching providers becomes a config change rather than a refactor.
GET /v1/models to enumerate available models, then POST /v1/chat/completions with the model id chosen by the user
RAG Pipeline with Hosted Embeddings
Build a retrieval-augmented chatbot that produces embeddings on AI/ML API. Generate vectors with /v1/embeddings, store them in a vector database, and use /v1/chat/completions for the final answer. The single Bearer credential covers both calls.
POST each document chunk to /v1/embeddings, store the vectors, retrieve nearest neighbours at query time, then POST /v1/chat/completions with the retrieved context
Voice In, Voice Out Assistant
Implement a voice assistant that listens, reasons, and replies in audio. Use /v1/audio/transcriptions to turn speech into text, /v1/chat/completions to generate a reply, and /v1/audio/speech to produce the audio response. The whole loop sits behind one API key.
POST audio to /v1/audio/transcriptions, send the text to /v1/chat/completions, then POST the reply to /v1/audio/speech
Image Generation from Marketing Copy
Generate hero images directly from marketing copy by passing the brief to /v1/images/generations. The single endpoint returns image URLs that can be used in landing-page generators or design tooling without managing a separate image generation provider.
POST /v1/images/generations with the marketing brief as the prompt and link the returned URL on the page
Agent-Driven Multi-Modal Pipeline via Jentic
An AI agent decides at runtime whether to call chat, embeddings, image, or audio endpoints. Through Jentic the agent searches for the right operation, the AI/ML API Bearer token is supplied from the vault, and the agent never sees the raw token.
Use Jentic search 'generate a chat completion', execute /v1/chat/completions, and chain /v1/embeddings if retrieval is needed
Key Endpoints
7 endpoints — jentic publishes the only available openapi specification for ai/ml api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/v1/chat/completions
Generate a chat completion
POST
/v1/completions
Generate a text completion
POST
/v1/embeddings
Create embedding vectors
POST
/v1/images/generations
Generate an image from a text prompt
POST
/v1/audio/transcriptions
Transcribe audio to text
POST
/v1/audio/speech
Synthesize speech from text
GET
/v1/models
List available models
POST
/v1/chat/completions
Generate a chat completion
POST
/v1/completions
Generate a text completion
POST
/v1/embeddings
Create embedding vectors
POST
/v1/images/generations
Generate an image from a text prompt
POST
/v1/audio/transcriptions
Transcribe audio to text
POST
/v1/audio/speech
Synthesize speech from text
GET
/v1/models
List available models
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
AI/ML API
- AI-Aware (B)67/100
100
Foundational Compliance63
Developer Experience & Jentic Compatibility44
AI-Readiness & Agent Experience94
Agent Usability60
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
99%
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
0%
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.
44
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: DSignals: 4
28%
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.
50%
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.
60
Security
Trust, risk posture, and security compliance.
Grade: B-Signals: 1
60%
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 AI/ML API by hand means handling its bearer token, matching each request to the right chat, embedding, image, or audio endpoint, and writing your own retry logic. Through Jentic you install once, import AI/ML API from the API Directory, store the token once, and your agent calls it.
Permission scoping
AI/ML API carries its model and inputs in the request body, so scope your agent by operation: limit it to the operations it needs, such as /v1/chat/completions or /v1/embeddings, and leave out image or audio endpoints it does not use.
Credential isolation
Your AI/ML API bearer token 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 'generate a chat completion', and Jentic returns the AI/ML API /v1/chat/completions operation with its messages and model 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 AI/ML API through Jentic.
Why is there no official OpenAPI spec for AI/ML API?
AI/ML API does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call the AI/ML API 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 AI/ML API use?
HTTP Bearer authentication. The Bearer token is sent in the Authorization header. Through Jentic the token is held in the vault and never enters the agent's context.
Can I run chat completions with multiple models?
Yes. POST /v1/chat/completions with the model id you want to use. Call GET /v1/models to discover which model identifiers are currently supported.
What are the rate limits for the AI/ML API?
The OpenAPI spec does not declare explicit rate limits. As with most LLM gateways, treat HTTP 429 responses as a signal to back off, and watch for per-model concurrency hints in the response body.
How do I generate embeddings through Jentic?
Run pip install jentic, search 'create text embeddings', and execute /v1/embeddings with your input strings. The response returns vectors ready to store in a vector database. Run it through Jentic One, the self-hosted execution layer.
Does the API cover both image generation and audio?
Yes. /v1/images/generations produces images from text prompts, /v1/audio/transcriptions converts audio to text, and /v1/audio/speech synthesises speech from text - all behind the same Bearer token.
Can I limit what my agent is allowed to do with the AI/ML API?
Yes. Because AI/ML API carries the model and inputs in the request body rather than in separate scopes, you scope the agent by operation in your own self-hosted Jentic One instance, where your rules decide which endpoints and credentials it may use. For example, you can allow only /v1/chat/completions and /v1/embeddings while leaving out /v1/images/generations, /v1/audio/transcriptions, and /v1/audio/speech that the agent does not need. The single stored bearer token is injected at execution time for the operations you permit, so the agent never sees the raw credential.
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