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.
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.
1
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" | 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%2Forq.ai%2Forq" | 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.
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}
Detect, redact, and restore personal data in text with the /v2/pii operations
Query traces and logs with OQL over a from and to time window, and fetch a trace's spans and conversation
Invoke a custom evaluator against a query, output, and reference through /v3/evaluators/{id}/invoke
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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
O
orq.ai API
- Foundational (C)57/100
73
Foundational Compliance59
Developer Experience & Jentic Compatibility50
AI-Readiness & Agent Experience43
Agent Usability75
Security73
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
73
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).
0%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
59
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: C+Signals: 4
3%
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.
50
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.
0%
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.
43
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.
75
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).
73
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A-Signals: 1
73%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>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.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
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