AgentQL Suite for Web Data Extraction and Automation
Connect your AI to the entire web with a natural language query language and Playwright integrations.
- Source repo
- tinyfish-io/agentql
- Stars
- ★ 1.5k
- Last updated
- 10d ago
- License
- MIT
- Primary language
- Python
- FA score
- 42/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A data analyst wants to scrape product prices from multiple e-commerce sites using natural language queries and export to CSV.
- Not a fit if
- Teams that need fully offline use; queries rely on AgentQL cloud services
- Users unwilling to pay for API quotas or a hosted plan
- Teams needing a mature tool with a large community and docs
- Source review
- 42/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
AgentQL is a suite of tools that enables AI agents to interact with the web through a natural language query language, Python and JavaScript SDKs, a browser debugger, and a REST API. It extracts structured data from live websites, including authenticated and dynamically generated content. Users define queries with natural language, and AgentQL pinpoints elements and data. The suite integrates seamlessly with Playwright, offering features like cross-site compatibility, self-healing queries, and structured output. It also provides integrations with agent frameworks like LangChain and Zapier, and an MCP server.
AgentQL's Python and JavaScript SDKs allow developers to write scripts that use AgentQL queries to interact with web pages. The REST API enables executing queries without an SDK. The browser extension debugger lets users refine queries in real-time. The query language supports extracting data and elements, applying transforms, and defining structured output. Example scripts in the repository cover various operations: closing cookie dialogs, comparing product prices, handling infinite scroll, logging into sites, saving and loading auth sessions, using headless browsers, and collecting YouTube comments.
- A data analyst wants to scrape product prices from multiple e-commerce sites using natural language queries and export to CSV.
- A developer needs to automate a workflow involving form submission and data extraction from a dynamic web page.
- A researcher wants to collect comments from YouTube or news headlines for sentiment analysis.
- A QA engineer uses Playwright and AgentQL to create resilient UI tests that adapt to changes.
- A business user needs to extract data from authenticated corporate portals without dealing with complex authentication flows.
- A developer wants to quickly integrate web data extraction into an existing application via the REST API.
How do you install or deploy this agent?
Install the Python SDK with pip install agentql. Install the JavaScript SDK with npm install agentql. Ensure Playwright is installed and browsers are configured. Obtain an API key from agentql.com.
How do you use this agent?
Set the AGENTQL_API_KEY environment variable. In Python, import agentql, start a session, and use query or extract methods. Refer to the examples directory, such as first_steps, for complete usage.
What are this agent's strengths and limitations?
- Natural language queries reduce the learning curve for web scraping.
- Cross-site compatibility enables reuse of queries across similar websites.
- Structured output simplifies data integration.
- Integration with Playwright supports complex automation scenarios.
- Extensive examples cover common use cases.
- Relies on AgentQL cloud services, which may introduce API quotas and costs.
- The query language and platform are relatively new, with limited community support.
- AI-based parsing may produce unexpected results on complex pages.
- Requires Playwright and browser setup, adding environmental complexity.
- MIT license applies to code, but usage may be subject to service terms.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AgentQL Suite for Web Data Extraction and Automation This agent | 42 · Major gaps | Library / SDKFreemium | ★ 1.5k | 10d ago | Python | — |
| CamoFox Browser Server | 72 · Some gaps | Self-hosted serviceFree | ★ 424 | 27d ago | JavaScript | Claude Code |
| Stagehand: The AI Browser Automation Framework | 47 · Major gaps | Library / SDKFree + model costs | ★ 26k | today | TypeScript | — |
| wigolo | 54 · Major gaps | CLIFree + model costs | ★ 5.5k | 22d ago | TypeScript | Codex · Claude Code · OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: MIT license present, but publisher unverified; README claims support for authenticated pages and remote browsers, implying sensitive data handling, but no specific security mechanisms or least-privilege details. Deductions: no user confirmation mechanism, insufficient data flow transparency, missing sensitive data handling details, dependency security not explicit, external effects (e.g., automation actions) not described, rollback mechanism absent, source attribution only via license.
Evidence: README and examples are structurally consistent, but no test results or runtime verification. Deductions: dependency availability not explicit (e.g., Playwright versions), failure messages not documented.
Evidence: README lists multiple use cases and examples, but capability boundaries (e.g., limitations) not defined. Deductions: trigger precision (e.g., query language specifics) not detailed, environment fit (e.g., browser versions) not explicit.
Evidence: README provides installation links and examples, but no full API docs or FAQ. Deductions: naming stability not explicit (e.g., version naming), known limitations not listed, changelog missing, maintenance responsibility not clear.
Evidence: README shows various output formats (e.g., CSV) and real-world scenarios, but no performance or cost data. Deductions: marginal value not quantified, cost-benefit not addressed.
Evidence: Claims (e.g., cross-site compatibility) lack specific tests or data. Deductions: claim traceability insufficient, cross-source corroboration missing, facts vs. inferences not clearly separated.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity unverified; assess supply chain risk carefully.
- No security audit or least-privilege details; evaluate sensitive data handling yourself.
- Dependency security not explicit; check dependency versions and vulnerabilities.
- Automation may affect external sites; ensure compliance.