Install Jentic One Beta

Connect the OmniMind API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the OmniMind 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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fomnimind.ai%2Fomnimind" | sh

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%2Fomnimind.ai%2Fomnimind" | 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 OmniMind API.

Create projects that act as isolated knowledge containers

Train a project from a list of web URLs, files via URL, or raw text

Stream training progress events to monitor long-running ingestion jobs

Manage curated QA pairs that override or complement the trained corpus

Search a project's indexed knowledge for relevant chunks

GET STARTED

Start building with OmniMind API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use OmniMind API for, with concrete tasks.

★ Internal Knowledge Base Q&A

An internal helpdesk uses POST /projects/{id}/training/website to ingest the company knowledge centre, then POST /projects/{id}/actions/ask to answer staff questions in natural language. The project-scoped design ensures different teams get answers from their own corpus rather than shared knowledge.

Create a project, train from 30 internal-docs URLs via POST /projects/{id}/training/website, then ask 'how do I request VPN access' via POST /projects/{id}/actions/ask.

Bulk Document Processing with Map and Reduce

When a project contains many records (support tickets, transcripts, contracts), POST /projects/{id}/actions/map applies a prompt to each record (e.g. extract sentiment) and POST /projects/{id}/actions/reduce aggregates the per-record outputs into a single answer. This is a managed equivalent of a RAG pipeline's batch-summarise step.

Run POST /projects/{id}/actions/map with a sentiment-extraction prompt across 200 support tickets, then POST /projects/{id}/actions/reduce to roll up overall sentiment trends.

Curated QA Override of Trained Corpus

Teams that want authoritative answers for specific questions add curated QA pairs via POST /projects/{projectId}/knowledge/qa. These pairs take precedence over the trained corpus when the question matches, so legal-vetted phrasings do not get rewritten by the model. Update or remove pairs via PATCH and DELETE on the same path.

POST a QA pair with question 'what is our SLA' and the legal-approved answer to /projects/{projectId}/knowledge/qa for the customer-success project.

AI Agent RAG Backend via Jentic

An AI agent integrated through Jentic uses OmniMind as its retrieval backend - no need to set up vector DBs, embeddings, or chunking. The agent calls POST /projects/{id}/actions/search to fetch relevant chunks or POST /actions/ask for direct answers. Jentic stores the bearer token or omni_key encrypted and injects it on each call.

Search Jentic for 'ask omnimind project', load /projects/{id}/actions/ask, and execute it with the question 'how do I configure SSO' against the docs project.

Key Endpoints

28 endpoints — jentic publishes the only available openapi specification for omnimind api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/projects

Create a new project

POST

/projects/{id}/training/website

Train a project from web URLs

POST

/projects/{id}/actions/ask

Ask a natural-language question scoped to a project

POST

/projects/{id}/actions/search

Search project knowledge

POST

/projects/{id}/actions/map

Apply a prompt to each project record

POST

/projects/{id}/actions/reduce

Reduce per-record outputs into a single result

POST

/projects

Create a new project

POST

/projects/{id}/training/website

Train a project from web URLs

POST

/projects/{id}/actions/ask

Ask a natural-language question scoped to a project

POST

/projects/{id}/actions/search

Search project knowledge

POST

/projects/{id}/actions/map

Apply a prompt to each project record

POST

/projects/{id}/actions/reduce

Reduce per-record outputs into a single result

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the OmniMind API by hand means choosing between a bearer token and the omni_key header, setting the right auth scheme on every call, and building each project action against app-api.omnimind.ai yourself. Through Jentic you install once, import the OmniMind API from the API Directory, store the credential once, and your agent calls it.

Permission scoping

OmniMind puts the project id in the URL path (/projects/{id}/actions/ask, /projects/{id}/actions/search), so a rule can pin your agent to one project: it can query and search that project and nothing else. You choose the operations it may call, so training ingestion or project creation are not included unless you add them.

Credential isolation

Your OmniMind bearer token or omni_key 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 'ask a question on omnimind' or 'train omnimind from a url', and Jentic returns the matching OmniMind action with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using OmniMind API through Jentic.

Why is there no official OpenAPI spec for OmniMind API?

OmniMind does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call OmniMind 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 OmniMind API use?

OmniMind supports two schemes: HTTP bearer (Authorization: Bearer ...) and the omni_key API-key header. Jentic stores either credential in its encrypted vault and chooses the configured one per project at call time.

Can I train an OmniMind project from a list of URLs?

Yes. POST /projects/{id}/training/website accepts a list of URLs and starts a training job. Use GET /projects/{projectId}/training/{trainingId}/stream to monitor progress in real time, or PATCH /projects/{id}/training/{trainingId}/cancel to abort if the wrong URLs were submitted.

What are the rate limits for the OmniMind API?

Rate limits are not declared in the OpenAPI spec. Training endpoints are bounded by ingestion throughput rather than request rate; action endpoints (ask, search, map, reduce) follow standard fair-use throttling.

How do I ask a question against a specific project through Jentic?

Search Jentic for 'ask question on omnimind project', and the SDK returns POST /projects/{id}/actions/ask. Provide the project ID and the question - the answer comes back grounded in that project's corpus.

Does OmniMind expose embeddings or chunk-level vectors directly?

Not via this REST API. The Search action returns ranked chunk content, but raw embedding vectors and chunking strategy are managed inside OmniMind. Use Pinecone or a similar vector DB if you need direct vector access.

Can I limit what my agent is allowed to do with the OmniMind API?

Yes. Because you run Jentic One yourself, your own rules decide which OmniMind operations and credentials the agent may use. OmniMind carries the project id in the URL path, such as /projects/{id}/actions/ask and /projects/{id}/actions/search, so you can pin the agent to a single project and let it only query and search that one. You also choose the operations it may call, so higher-impact actions like training ingestion or project creation stay off the table unless you explicitly add them.

OmniMind API

Name: OmniMind API
Brand: OmniMind API
Availability: InStock

Agent-ready OpenAPI document · curated by JenticAI/MLLanguage Modelsbearer, apiKey28 EndpointsREST

Know of an official OpenAPI document? Contribute it →

For Agents

Build per-project knowledge bases from URLs, files, and text, then ask, search, map, or reduce over them - a managed RAG layer for AI applications.

Use for: I need to create a new OmniMind project for our internal docs, Train a project from these 50 URLs of our help-centre articles, Ask the question 'what is the refund policy' against our support project, Search the project knowledge for chunks mentioning 'API key rotation'

Not supported: Does not handle raw embedding storage, model fine-tuning, or speech and vision tasks - use for project-scoped RAG ingestion and querying only.

Jentic publishes the only available OpenAPI specification for OmniMind API, keeping it validated and agent-ready. The OmniMind API gives applications a managed RAG-style platform for building project-scoped knowledge bases from URLs, files, and text, then querying them via search, ask, map, and reduce actions. It exposes 28 endpoints covering project CRUD, training ingestion (web URLs, file URLs, raw text), training progress streams, knowledge QA pair management, and four action endpoints (search, ask, map, reduce) plus a global ask. Authentication supports both bearer tokens and the omni_key header.

Jentic One on Github View OpenAPI Document

Connect the OmniMind API to your agent

Capabilities

Start building with OmniMind API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

OmniMind API