Gradient AI API

Name: Gradient AI API
Brand: Gradient AI API
Availability: InStock

✓ Official Vendor SpecAI/MLLanguage Modelsbearer22 EndpointsREST

For Agents

Run LLM completions, fine-tune models, generate embeddings, manage RAG collections, and call prebuilt AI blocks for sentiment, entities, summarisation, Q&A, and PDF extraction.

Connect the Gradient AI API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Gradient AI 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.

Install Jentic One Beta (Self-Hosted)

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

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 Gradient AI API.

Generate completions from a model with POST /models/{id}/complete

Fine-tune a base model on uploaded data through POST /models/{id}/finetune

Create and query embeddings via POST /embeddings

Build and populate RAG collections with /rag-collections and /rag-collections/{id}/files

GET STARTED

Start building with Gradient AI API

Explore with Jentic

View OpenAPI Document

Gradient AI is an LLM platform that combines model fine-tuning, completion and embedding generation, retrieval-augmented generation collections, and a library of prebuilt AI building blocks. Its 22-endpoint API covers the full lifecycle: list and create models, fine-tune them, run completions, generate embeddings, manage RAG collections and their indexed files, and call task-specific blocks for sentiment, entity extraction, summarisation, question answering, PDF extraction, and personalisation. Authentication is HTTP Bearer.

Use Cases

Patterns agents use Gradient AI API for, with concrete tasks.

★ Domain-Specific Fine-Tuning

Teams with proprietary data fine-tune a Gradient base model on customer support transcripts or domain corpora. POST /files uploads the training data, POST /models creates the new model, and POST /models/{id}/finetune kicks off the job. The resulting model is then served through the same /models/{id}/complete endpoint as base models, keeping inference code unchanged.

POST /files with a JSONL training set, POST /models to register the target model, then POST /models/{id}/finetune and poll until status reaches completion.

Retrieval-Augmented Generation Over Private Docs

POST /rag-collections creates a collection, POST /rag-collections/{id}/files indexes uploaded PDFs and HTML, and downstream completion calls scoped to the collection ground responses in the indexed material. This avoids hallucination on proprietary content and removes the need for a separate vector store. The agent still chooses which model handles the final completion.

Create a RAG collection, upload 50 PDFs via POST /rag-collections/{id}/files, then call POST /blocks/answer scoped to that collection for a user question.

Building Block Pipelines

Rather than write prompts from scratch, agents call prebuilt blocks: /blocks/sentiment for tone, /blocks/entity for entities, /blocks/summarize for summaries, /blocks/answer for grounded Q&A, /blocks/pdf for PDF extraction, and /blocks/personalize for tailored content. Each block returns a structured payload, making them easy to chain inside an agent's reasoning loop.

POST /blocks/pdf with a contract upload, then POST /blocks/entity on the extracted text to surface party names and dates.

Embedding Generation for Search

POST /embeddings turns a list of texts into vector embeddings used to power semantic search inside the agent's own pipeline. GET /embeddings lists available embedding models so the agent can pick a dimensionality and language match. This is for cases where the team owns the vector store and only wants embeddings as a service rather than a full RAG pipeline.

POST /embeddings with 200 article titles and store the returned vectors in pgvector for cosine search.

Agent Integration via Jentic

An agent that wants 'a fine-tuned customer support model with RAG over our help centre' uses Jentic to discover Gradient AI, walk through /files upload, /models creation, /models/{id}/finetune, /rag-collections, and finally /models/{id}/complete. The Bearer token sits in Jentic's vault, so credentials never enter the agent's prompt context.

Use the Jentic SDK to search 'fine tune a model and run rag completion', chain /files, /models, /models/{id}/finetune, /rag-collections, and /models/{id}/complete in sequence.

Key Endpoints

22 endpoints — gradient ai is an llm platform that combines model fine-tuning, completion and embedding generation, retrieval-augmented generation collections, and a library of prebuilt ai building blocks.

METHOD

PATH

DESCRIPTION

POST

/models/{id}/complete

Generate a completion from a model

POST

/models/{id}/finetune

Fine-tune a model on uploaded data

POST

/embeddings

Create embeddings from a list of texts

POST

/rag-collections

Create a RAG collection

POST

/rag-collections/{id}/files

Add files to a RAG collection

POST

/blocks/summarize

Summarise a document

POST

/blocks/answer

Answer a question grounded in a document

POST

/blocks/pdf

Extract structured content from a PDF

POST

/models/{id}/complete

Generate a completion from a model

POST

/models/{id}/finetune

Fine-tune a model on uploaded data

POST

/embeddings

Create embeddings from a list of texts

POST

/rag-collections

Create a RAG collection

POST

/rag-collections/{id}/files

Add files to a RAG collection

Why Jentic?

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

Credential isolation

Gradient AI's Bearer token sits in Jentic's MAXsystem vault. The Authorization header is injected at execution time, so the raw token never enters agent prompts, tool outputs, or transcripts.

Intent-based discovery

Agents search Jentic with intents like 'fine tune a model', 'create a rag collection', or 'extract entities from a document' and Jentic surfaces the right Gradient endpoint with its parameter schema, so the agent does not need to navigate 22 paths manually.

Time to first call

Direct integration: 2-3 days to wire fine-tuning, RAG indexing, and block calls with consistent error handling. Through Jentic: under 1 hour — the SDK execution flow handles the polling and id-passing between steps.

FAQs

Specific to using Gradient AI API through Jentic.

What authentication does the Gradient AI API use?

Gradient AI uses HTTP Bearer authentication (security scheme 'bearerAuth'). Issue a key in the Gradient AI dashboard and send it as Authorization: Bearer <token>. Through Jentic, the bearer token is stored in MAXsystem and injected at execution time so the agent never sees the raw secret.

Can I fine-tune my own model on Gradient AI?

Yes. POST /models creates a new model entry, POST /files uploads training data, and POST /models/{id}/finetune starts the job. After completion the resulting model is callable through POST /models/{id}/complete using the same shape as base models.

What are the rate limits for the Gradient AI API?

The OpenAPI spec does not declare per-endpoint rate limits. Limits are tied to your plan tier and to specific endpoints (completion, fine-tune, embeddings) which have separate concurrency caps. Use the Gradient AI dashboard to monitor usage.

How do I run a RAG completion through Jentic?

Search Jentic for 'create a rag collection and answer a question', chain POST /rag-collections, POST /rag-collections/{id}/files for indexing, and POST /blocks/answer to ask a grounded question. The Jentic SDK returns each step's response so the agent can pass ids forward.

What are 'blocks' in the Gradient AI API?

Blocks are prebuilt task endpoints — sentiment, entity, summarize, answer, pdf, and personalize. Each accepts a domain-specific input (text, document, persona) and returns structured output, so the agent does not need to author prompts from scratch for these tasks.

Is the Gradient AI API free?

Gradient AI offers a developer tier with limited monthly quota and paid tiers with higher concurrency, fine-tuning, and dedicated capacity. Pricing is published on gradient.ai and tiers are enforced at the API gateway.

Gradient AI API

Connect the Gradient AI API to your agent

Capabilities

Start building with Gradient AI API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs