AI/ML API APIs for AI Agents
AI/MLLanguage ModelsVisionJentic-Generated SpecsBearer tokenAPI keyAgent Ready
AI/ML API API Catalog
2 APIs across 1 product group.
AI/ML2
AI/ML APIRun chat completions, text completions, embeddings, image generation, audio transcription, and text-to-speech through one OpenAI-style endpoint set, and list supported models.~7 endpointsAIMLAPIRun OCR on documents and analyse images with vision features through two focused endpoints.~2 endpoints
Which AI/ML API API should I use?
| I want to... | Use | Why |
|---|---|---|
| Chat, text completions, embeddings, image generation, or audio transcription and speech | AI/ML API | The aiml-api spec exposes the OpenAI-style chat, completions, embeddings, images, and audio endpoints plus model listing. |
| Text extraction from document images or vision-feature analysis of images | AIMLAPI | The aimlapi spec exposes the focused /ocr and /vision endpoints for document and image understanding. |
| Turning an image into a text description | AIMLAPI | Use /vision on the aimlapi spec for image analysis; the aiml-api spec generates images from text rather than describing them. |
Install Jentic One Beta
Connect the AI/ML API APIs to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the AI/ML API APIs, 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://jentic.com/install.sh?src=apis&api=%2Fapis%2Faimlapi.com" | 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" | 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.
Once connected, ask your agent something like: “run inference and document analysis with AI/ML API”.
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring AI/ML API by hand means handling its account credential, matching each request to the right chat, embedding, image, audio, OCR, or vision endpoint across two specs, and writing your own retry logic. With Jentic you install Jentic One once, add AI/ML API from the Jentic directory, store the credential once, and your agent calls any endpoint.
Permission scoping
AI/ML API carries its model and inputs in the request body rather than in separate scopes, so you scope the agent by operation in your own self-hosted Jentic One instance. Your rules decide which endpoints and credentials it may use, for example allowing only /v1/chat/completions and /ocr while leaving out image, audio, and vision endpoints it does not need.
Credential isolation
FAQs
Specific to using AI/ML API APIs through Jentic.
What can I build across AI/ML API's specs?
The broader spec runs chat and text completions, embeddings, image generation, audio transcription, and text-to-speech, and lists supported models. The focused spec adds document OCR and vision-feature image analysis. Together they cover text, image, and audio inference plus document and image understanding under one account.
Do the two specs share one account and credential?
Yes. Both call the same api.aimlapi.com base URL and use one AI/ML API account credential. The broader spec documents it as an HTTP Bearer token and the OCR and vision spec as an apiKey scheme, but both resolve to the same account.
Which spec should I use for a given task?
Use the aiml-api spec for chat, completions, embeddings, image generation, and audio. Use the aimlapi spec for extracting text from document images with OCR and for analysing images with vision features. Many workflows chain both, such as running OCR then answering questions with a chat completion.
Can I switch models without changing my integration?
Yes on the broader spec. Call GET /v1/models to discover which model identifiers are currently supported, then pass the chosen model id to the chat, completions, or embeddings endpoints. Switching backends is a config change rather than a code rewrite.
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