AgentQL
AgentQL connects LLMs and AI agents to the entire web through a specialized query language, REST API, and Python/JavaScript SDKs. It enables web scraping, data extraction, and browser automation using natural language queries that are self-healing — adapting automatically to page layout changes. AgentQL supports structured data extraction from web pages, PDF documents, and images, and integrates with LangChain, LlamaIndex, MCP, Zapier, and Google ADK.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles AgentQL the way a machine reads it — 50 machine-readable artifacts across 5 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — AgentQL scores 30.7/100 (thin), with a separate agent-readiness read of 34/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-10 · rubric v0.20.0
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title="AgentQL on API Evangelist — API profile and Kin Score">
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</a>More shapes, themes and sizes → · Score as JSON · How badges work
How we profile AgentQL
Each block below is one kind of artifact we hold for AgentQL. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 5
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
AgentQL Remote Browser Sessions API
Create and manage remote Chrome browser sessions with Chrome DevTools Protocol (CDP) access for authenticated web automation, stealth browsing, and complex multi-step interactions.
AgentQL Query Document API
Extract structured data from PDF documents and images (JPEG, PNG) using AgentQL query language or natural language prompts. Useful for processing invoices, reports, and other do...
AgentQL Query Data API
Extract structured data from web pages using AgentQL queries
AgentQL Query Document API
Extract structured data from PDF and image documents
AgentQL Remote Browser API
Manage remote Chrome browser sessions with CDP access
Postman Collections 3
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Open Collections 4
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
API Collection
OPEN COLLECTIONAgentQL Query Data API
OPEN COLLECTIONAgentQL Query Data Query Document API
OPEN COLLECTIONAgentQL Query Data Remote Browser API
OPEN COLLECTIONMCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
MCP Server
MCP SERVERPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Agentql Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Agentql Finops
FINOPSFeatures 7
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Natural Language Query Language
A specialized query language that uses natural language to locate and extract web elements without requiring XPath, CSS selectors, or regex.
Self-Healing Queries
AI-powered queries automatically adapt to page layout changes, eliminating brittle scrapers that break on site updates.
REST API
Browserless data extraction from public URLs via a REST API requiring only an API key and query parameters.
PDF and Image Parsing
Extract structured data from PDF documents, JPEG, and PNG images using the same query language as web extraction.
Remote Browser Sessions
Managed Chrome browser sessions with CDP access for authenticated browsing, stealth mode, and complex multi-step web automation.
Playwright Integration
Python and JavaScript SDKs extend Playwright with AgentQL query capabilities for AI-powered browser automation.
Browser Debugger Extension
Chrome extension for real-time query testing and optimization during development.
Scroll within the panel for all 7 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Agentql Context
JSON-LDSpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
AgentQL API Rules
SPECTRALJSON Schema 7
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
CreateSessionRequest
JSON SCHEMACreateSessionResponse
JSON SCHEMAQueryDataRequest
JSON SCHEMAQueryDataResponse
JSON SCHEMAQueryDocumentRequest
JSON SCHEMAQueryParams
JSON SCHEMAResponseMetadata
JSON SCHEMAScroll within the panel for all 7 ·
JSON Structure 7
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Agentql Create Session Request Structure
JSON STRUCTUREAgentql Create Session Response Structure
JSON STRUCTUREAgentql Query Data Request Structure
JSON STRUCTUREAgentql Query Data Response Structure
JSON STRUCTUREAgentql Query Document Request Structure
JSON STRUCTUREAgentql Query Params Structure
JSON STRUCTUREAgentql Response Metadata Structure
JSON STRUCTUREScroll within the panel for all 7 ·
Examples 2
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
E-Commerce Price Monitoring
Extract product names, prices, and availability from e-commerce sites for competitive intelligence and price tracking.
Job Board Aggregation
Collect job listings, requirements, and company information from multiple job boards into a unified dataset.
Social Media Content Harvesting
Extract posts, metrics, and profile data from social media platforms for analysis and reporting.
Document Data Extraction
Parse invoices, contracts, and reports in PDF format to extract structured data for downstream processing.
AI Agent Web Access
Enable AI agents to access and extract data from any website as part of automated research and task completion workflows.
Lead Generation
Automate the collection of contact information, company data, and other business intelligence from public web sources.
Resources
Every other property we hold for AgentQL — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Build 5
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/agentql · machine-readable index on apis.io
This is an independent, third-party profile of AgentQL, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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