AgentQL API

Name: AgentQL API API
Brand: AgentQL API
Availability: InStock

★ Only Publicly Available SpecAI/MLMl InferenceapiKey8 EndpointsREST

For Agents

Extract structured fields from any web page using natural language or AgentQL queries. Run inside Tetra remote browser sessions when sites require interaction or login.

Quickstart

Get started with AgentQL API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"extract structured data from a web page"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with AgentQL API API.

Query structured fields from public web pages without writing CSS or XPath selectors

Run AgentQL queries inside a Tetra remote browser session for sites that require login or JavaScript interaction

Use natural language prompts as an alternative to AgentQL syntax for ad-hoc extraction tasks

Track API key usage and remaining quota with the /v1/usage endpoint

GET STARTED

Start building with AgentQL API API

Explore with Jentic

View OpenAPI Spec

Use for: I need to extract product prices from an e-commerce listing page, Get the headline and author from a news article URL, Find all job postings on a careers page and return them as a list, Set up a Tetra browser session to log in and scrape behind authentication

Not supported: Does not handle proxy rotation, CAPTCHA solving, dataset storage, or large-scale crawl scheduling — use for structured extraction from individual URLs only.

Jentic publishes the only available OpenAPI specification for AgentQL API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for AgentQL API, keeping it validated and agent-ready. AgentQL extracts structured data from web pages and documents using AgentQL query syntax or natural language prompts. The 0.1.0 spec exposes both versioned (/v1) and unversioned routes for the query-data endpoint plus the Tetra remote Chrome browser session API. Each call returns parsed fields keyed by the names declared in the query, removing brittle CSS or XPath selectors from agent code.

Use Cases

Patterns agents use AgentQL API API for, with concrete tasks.

★ Web Data Extraction Without Selectors

Replace brittle CSS-selector scrapers with semantic AgentQL queries that survive site redesigns. The agent posts a query to /v1/query-data with the target URL, and AgentQL returns named fields matching the query shape. Suited to monitoring product catalogs, news feeds, or directory pages where layout changes frequently.

Call POST /v1/query-data with url='https://news.ycombinator.com' and query='{ stories[] { title link } }' and return the parsed stories array

Authenticated Page Scraping via Tetra

Use a Tetra remote browser session to log into a SaaS dashboard, navigate to a report page, and run an AgentQL query to extract the table. The agent calls POST /v1/tetra/sessions to provision the session, drives the browser, and then issues query-data calls scoped to the session. Useful for pulling reports from internal tools that have no public API.

Create a Tetra session, sign in to a vendor dashboard with stored credentials, then run a query-data extraction against the analytics page

Document Field Extraction

Pass PDF or image URLs to AgentQL with a natural-language prompt to pull invoice numbers, totals, or contract terms into a structured object. The agent does not need to run OCR or build per-template parsers — AgentQL returns the named fields directly. Suited for processing supplier invoices or scanned forms at moderate volume.

Submit a PDF invoice URL with the prompt 'extract invoice_number, total_amount, due_date' and store the parsed fields in the accounting system

Agent-Driven Research Workflows

Let a Jentic-connected agent decide on the fly which URLs to query and what fields to extract during a research task. The agent searches Jentic for the AgentQL query operation, loads the input schema, and issues queries iteratively as it explores links. This replaces hand-coded scraping pipelines for one-off research jobs.

Given a competitor's homepage URL, recursively query for product pages and extract pricing into a comparison table

Key Endpoints

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

METHOD

PATH

DESCRIPTION

POST

/v1/query-data

Run an AgentQL query against a URL

POST

/v1/tetra/sessions

Create a remote browser session

GET

/v1/usage

Get account API usage

GET

/v1/tetra/usage

Get Tetra session usage

POST

/v1/query-data

Run an AgentQL query against a URL

POST

/v1/tetra/sessions

Create a remote browser session

GET

/v1/usage

Get account API usage

GET

/v1/tetra/usage

Get Tetra session usage

Why though Jentic?

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

Credential isolation

AgentQL API keys are stored encrypted in the Jentic vault. The X-API-Key header is injected at execution time so the raw key never enters the agent context or chat logs.

Intent-based discovery

Agents search by intent (e.g. 'extract data from a web page') and Jentic returns the AgentQL query operations with their input schemas and accepted query syntax.

Time to first call

Direct AgentQL integration: a few hours for auth, query construction, and Tetra session lifecycle. Through Jentic: under 15 minutes — search, load, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Browserless

Headless Chrome as a service for full-page scraping with selector-driven extraction

Choose Browserless when the agent already has CSS or XPath selectors and needs raw HTML or screenshots rather than a structured query result.

Alternative

Apify API

Pre-built scrapers (Actors) for common sites with persistent dataset storage

Choose Apify when the agent needs a ready-made scraper for a specific site (Google, Amazon, Twitter) and dataset storage rather than a query language.

Complementary

ZenRows

Proxy rotation and anti-bot bypass for hard-to-scrape sites

Use ZenRows in front of AgentQL when target sites block direct requests; route the page through ZenRows then pass the rendered HTML to AgentQL.

FAQs

Specific to using AgentQL API API through Jentic.

Why is there no official OpenAPI spec for AgentQL API?

AgentQL does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call AgentQL API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the AgentQL API use?

AgentQL uses an API key passed in the X-API-Key request header. Through Jentic the key is held in the credential vault and injected at execution time, so the raw key never enters the agent's context window.

Can I scrape sites that require a login with the AgentQL API?

Yes, via the Tetra remote browser. POST /v1/tetra/sessions to provision a Chrome session, drive it to the login flow, then issue /v1/query-data calls scoped to that session id. Static-page queries against /v1/query-data alone do not handle authentication.

What are the rate limits for the AgentQL API?

AgentQL enforces per-plan request and Tetra-minute quotas. Call GET /v1/usage to read the current period's consumption. The API does not document a fixed requests-per-second limit; build retry-with-backoff for any 429 response.

How do I extract a structured product list from a URL through Jentic?

Use the Jentic search query 'extract structured data from a web page'. Jentic returns the POST /v1/query-data operation; load its schema, supply the url and an AgentQL query like '{ products[] { name price } }', and execute. The response contains the parsed products array.

Does AgentQL support natural-language extraction without writing AgentQL syntax?

Yes. The /v1/query-data endpoint accepts a prompt parameter as an alternative to a structured query. The result is still returned as JSON, but the field shape is inferred from the prompt rather than declared explicitly.