Install command
Not provided
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
Required checks are still incomplete. Finish source and safety verification before adopting this resource.
0
58
—
No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
Registry metadata indicates a reviewed listing.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
No safety notes listed.
Privacy notes presentRequired
No privacy notes listed.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Install command
Not provided
Config snippet
Not provided
Copy snippet
Provided
Prerequisites
None
Platforms
1 listed
Install type
Copy & paste
Adoption plan
Current risk score 44/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
Listing has review metadata.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes missing; review source code paths before execution.
Review privacy notesRequired
Privacy notes missing; inspect network/data behavior manually.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Missing required evidence: Safety notes. Risk score 36.
Source repository/provenance is listed.
Required in this preset
Review metadata is present.
Required in this preset
Safety notes are missing.
Required in this preset
Privacy notes are missing.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required gaps: Safety notes
Decision timeline
Blocking gaps: Review safety notes. Risk 32.
triage
Source/provenance metadata is available.
triage
Review metadata is available.
verify
Safety notes are missing.
verify
Privacy notes are missing.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
Blockers: Review safety notes
## Editorial notes
Apify is useful for agent builders who need reusable data extraction workflows, web automation actors, and hosted datasets.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.Apify is useful for agent builders who need reusable data extraction workflows, web automation actors, and hosted datasets.
Editorial listing. No paid placement or affiliate link is used.
Apify side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
| Field | Web automation and scraping platform with actors, datasets, APIs, and integrations for data extraction workflows. Open dossier | Open-source, self-hostable workflow automation platform with AI workflows, TypeScript pieces, human-in-the-loop steps, and a built-in MCP server. Open dossier | Open-source browser automation library for building AI agents that can navigate, click, type, and inspect websites. Open dossier | Open-source, LLM-friendly Python web crawler and scraper that turns web pages into clean, LLM-ready Markdown for RAG, agents, and data pipelines, with an async browser pool, caching, structured extraction, and adaptive deep crawling. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | — | oktofeesh1 | — | davion-knight |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy · | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | ||||
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Apify | Activepieces | Browser Use | unclecode |
| Added | 2026-04-27 | 2026-06-03 | 2026-04-27 | 2026-07-09 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | — missing | ✓Activepieces flows can send messages, call APIs, write records, publish webhooks, run code, and trigger cross-system side effects, so production flows need tests, approvals, rollback paths, and rate-limit controls. The built-in MCP server can let AI assistants build flows, manage tables, inspect runs, test automations, and publish changes; enable only the needed tool categories and keep project scope tight. Custom TypeScript pieces and code steps should be reviewed like application code, especially when they handle secrets, filesystem access, network calls, or business-critical integrations. | ✓Browser Use drives a real browser and can navigate, click, type, and submit forms autonomously; run it against trusted sites and review actions before granting access to logged-in sessions or sensitive accounts. | ✓Crawl4AI fetches and renders web pages you point it at, running a headless browser that executes page scripts, so crawl only sites you trust to run and process. Crawled content is untrusted input; when its Markdown or extracted text is fed to an LLM or agent, treat it as a prompt-injection surface and constrain what the agent may do with it. Respect each site's terms of service, robots directives, and rate limits, and avoid crawling content you are not permitted to access. If you run the Docker API server, keep authentication enabled and do not expose it on a public interface without protection; recent releases harden it as secure-by-default. Keep production crawling permissions and scope narrower than quickstart examples, and set timeouts and limits for long or deep crawls. |
| Privacy notes | — missing | ✓Workflows can process prompts, customer records, emails, documents, form responses, table data, app payloads, webhooks, run logs, error traces, and AI-generated outputs. Activepieces connections may store OAuth tokens, API keys, account identifiers, webhook URLs, and service credentials; avoid exposing them in prompts, logs, MCP tool output, screenshots, or exported flows. Self-hosted deployments still need retention, backup, database, Redis, worker isolation, outbound network, telemetry, and access-control policies for all flow and run data. | ✓Page content, screenshots, and DOM data are sent to the configured LLM provider to plan actions, and agents can read and submit data on authenticated sites; control credentials and which pages agents can access. | ✓Crawled pages, extracted text, and generated Markdown can contain personal or proprietary data from the sites you visit; handle that output under normal data-handling policies. LLM-based extraction sends page content to the configured model provider, which processes it under its own terms; local models keep that processing on your machine. Caches, saved crawl outputs, and logs can retain fetched content and metadata, so choose retention and access controls deliberately. Model-provider keys, crawl configurations, and stored outputs should be kept out of version control and access-controlled like other operational data. |
| Prerequisites | — none listed |
| — none listed |
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| Install | — | — | — | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
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