Install Jentic One Beta

Connect the orq.ai API to your agent

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

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%2Forq.ai%2Forq" | 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%2Forq.ai%2Forq" | 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 orq.ai API.

Invoke a prompt deployment by its key with inputs and messages through /v2/deployments/invoke, or stream it through /v2/deployments/stream

Route chat completions, embeddings, rerank, and moderation requests through the /v2/router endpoints

Search a knowledge base with a query, top_k, threshold, and search_type, and manage its datasources and chunks

Version prompts and tools, then read back a specific prompt version through /v2/prompts/{prompt_id}/versions/{version_id}

GET STARTED

Start building with orq.ai API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use orq.ai API for, with concrete tasks.

★ Agents calling governed LLM deployments through Jentic

An agent that needs an LLM answer calls an orq.ai deployment instead of a raw model, so the prompt, model choice, and guardrails configured in orq.ai apply. Through Jentic the agent finds the DeploymentInvoke operation by intent, passes the required key plus inputs or messages, and gets the completion back, while your own Jentic One instance injects the orq.ai API key at call time.

POST /v2/deployments/invoke with key 'customer-support-answer' and inputs {"question": "How do I reset my password?"}, then return the generated message

Retrieval over a managed knowledge base

Teams building retrieval-augmented answers keep documents in an orq.ai knowledge base, add datasources, and let orq.ai chunk them. The SearchKnowledge operation takes a query plus optional top_k, threshold, and search_type, and returns matching chunks, while ListChunks and UpdateChunkEnabled let an operator inspect or switch off chunks that produce bad answers.

POST /v2/knowledge/{knowledge_id}/search with query 'refund policy for annual plans' and top_k 5, then summarise the returned chunks

PII redaction before model calls

Applications that send user text to models often need personal data removed first. The PIIRedact operation takes text with optional language, entities, and threshold settings and returns redacted text, PIIDetect reports what was found, and PIIRestore puts the original values back into a model's response when the caller is allowed to see them.

POST /v2/pii/redact with text 'Call Maria Lopez at +34 600 123 456' and language 'en', then pass the redacted text to the next step

Trace and log investigation with OQL

When an LLM feature misbehaves, engineers query orq.ai traces with OQL over a time window, open the failing trace, and read its spans and conversation. TracesQueryOql requires from, to, and oql, and TracesGetConversation returns the exchange the user saw, so an agent can assemble an incident summary from real telemetry.

POST /v3/traces/query with from '2026-11-11T09:00:00Z', to '2026-11-11T10:00:00Z', and an oql filter for error spans, then GET /v3/traces/{trace_id}/spans for the first result

Key Endpoints

313 endpoints — the orq.

METHOD

PATH

DESCRIPTION

POST

/v2/deployments/invoke

Invoke a deployment by key

POST

/v2/router/chat/completions

Create a routed chat completion

POST

/v2/router/embeddings

Create embeddings

POST

/v2/knowledge/{knowledge_id}/search

Search a knowledge base

GET

/v2/prompts

List prompts

POST

/v2/pii/redact

Redact PII from text

POST

/v3/traces/query

Query traces with OQL

POST

/v3/evaluators/{id}/invoke

Invoke a custom evaluator

POST

/v2/deployments/invoke

Invoke a deployment by key

POST

/v2/router/chat/completions

Create a routed chat completion

POST

/v2/router/embeddings

Create embeddings

POST

/v2/knowledge/{knowledge_id}/search

Search a knowledge base

GET

/v2/prompts

List prompts

POST

/v2/pii/redact

Redact PII from text

POST

/v3/traces/query

Query traces with OQL

POST

/v3/evaluators/{id}/invoke

Invoke a custom evaluator

Why Jentic?

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

Setup

Wiring orq.ai by hand means bearer auth, choosing between the v2 and v3 surfaces, and handling streaming deployments and router calls yourself. Through Jentic you install once, import orq.ai from the API Directory, store the API key once, and your agent calls it.

Permission scoping

orq.ai puts resource ids in the URL path, such as /v2/knowledge/{knowledge_id}/search and /v2/datasets/{dataset_id}/datapoints, so a rule can pin your agent to one knowledge base or one dataset. You choose the operations it may call, so destructive ones like DeleteDataset or ApiKeyCreate are not included unless you add them.

Credential isolation

Your orq.ai API 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 'invoke a deployment' or 'search a knowledge base', and Jentic returns the matching orq.ai operation with its input schema, so the agent picks the right one of hundreds without reading the reference.

FAQs

Specific to using orq.ai API through Jentic.

What authentication does the orq.ai API use?

The orq.ai API authenticates with a bearer token per its OpenAPI spec: your orq.ai API key sent as Authorization: Bearer, with the scheme marked as JWT format. A few model catalog lookups, such as GET /v2/model-catalog, are declared without security. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time, so it never enters the agent's prompt or logs.

Can I search an orq.ai knowledge base with the API?

Yes. POST /v2/knowledge/{knowledge_id}/search requires a query and accepts top_k, threshold, search_type, filter_by, and rerank_config. To keep results clean you can list a datasource's chunks and switch individual chunks off with PATCH /v2/knowledge/{knowledge_id}/datasources/{datasource_id}/chunks/{chunk_id}/enabled.

What are the rate limits for the orq.ai API?

The OpenAPI spec does not specify rate limits. Check the orq.ai documentation at docs.orq.ai for current limits on your plan, and use the budgets operations to cap spend per workspace.

How do I invoke an orq.ai deployment through Jentic?

Install Jentic One, add the orq.ai API from the Jentic API Directory, and store your orq.ai API key once. Your agent searches for 'invoke an orq.ai deployment', gets DeploymentInvoke with its input schema, and calls POST /v2/deployments/invoke with the required key and its inputs or messages. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Which orq.ai agent endpoints are deprecated?

The spec marks the older agent execution paths as deprecated, including POST /v2/agents/{key}/task, POST /v2/agents/run, POST /v2/agents/stream-run, and the /v2/agents/{agent_key}/responses operations. Agent create, list, retrieve, update, and delete under /v2/agents remain current, and schedules live under /v3/agents/{agent_key}/schedules.

Is there an orq.ai MCP server?

You don't need an MCP server to give your agent the orq.ai API. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it. With 313 operations in this spec, discovering them on demand keeps hundreds of tool definitions out of the agent's context.

Can I limit what my agent is allowed to do with the orq.ai API?

Yes. Write a rule that allows only POST /v2/deployments/invoke and the search operation under one knowledge_id, and your agent can answer from one knowledge base and call deployments, but cannot delete datasets, mint API keys, or change budgets. Every call it makes is logged by your own Jentic One instance.

orq.ai API

AI ReadinessFoundational (C)57/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLLanguage Modelsbearer313 EndpointsREST

For Agents

Call LLMs through orq.ai's router or invoke a saved deployment by key, search a knowledge base, run evaluators against outputs, redact PII, and query traces and logs to see how an LLM application behaved.

Use for: I need to call my orq.ai deployment with a customer's question, Search the support knowledge base for the refund policy, Redact personal data from this transcript before storing it, Find all traces from the last hour where the model returned an error

Not supported: Does not host your own model weights, train or fine-tune base models, or serve as a vector database outside orq.ai knowledge bases. Use for running and operating LLM applications on the orq.ai platform only.

The orq.ai API runs and operates LLM applications on the orq.ai platform: it routes chat completions, embeddings, rerank, moderation, speech, image, and OCR calls across model providers, and invokes prompt deployments by their key. Around that inference layer it manages the building blocks of an LLM app, including versioned prompts and tools, agents with schedules, knowledge bases with datasources, chunks, and search, memory stores, and datasets of datapoints for evaluation. Operations teams can query logs and traces with OQL, run evaluators, redact PII from text, and set budgets, routing rules, guardrail rules, and alerts for a workspace.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

90%

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).

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: C+Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

34%

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.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: C-Signals: 4

50%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

89%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

62%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: D-Signals: 1

43%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: A-Signals: 1

75%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: A-Signals: 1

73%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the orq.ai API to your agent

Capabilities

Start building with orq.ai API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

orq.ai API

Jentic AI Readiness Score

orq.ai API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness