AI/ML API

Name: AI/ML API
Brand: AI/ML API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLLanguage Modelsbearer7 EndpointsREST

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.

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 | sh

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 instance

Jentic 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

GET STARTED

Start building with AI/ML API

Explore with Jentic

View OpenAPI Document

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.

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

Why Jentic?

Three things that make agents converge on Jentic-routed access.

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.

Time to first call

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.

AI/ML API

Connect the AI/ML API to your agent

Capabilities

Start building with AI/ML API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs