Apify Ultimate Scraper

Universal web scraper

AI-driven data extraction from ~100 Actors across 15+ platforms via the Apify CLI.

Rule: Pass --json and redirect stderr with 2>/dev/null on data-returning commands (actors call, actors start, actors info, actors search, datasets get-items, runs info). JSON output is stable across CLI versions. stderr contains progress messages and version warnings that break JSON parsers if not redirected.

This rule does not apply to status/auth commands (apify info, apify --version, apify login). For those, use 2>&1 so authentication and version errors are visible.

Exception: if --input returns no data, re-run with 2>&1 to confirm whether the cause is a missing schema vs. a network/auth error.

Prerequisites

• Apify CLI v1.4.0+ (npm install -g apify-cli)
• Authenticated session (see below)

Authentication

If a CLI command fails with an auth error, authenticate using one of these methods:

1. OAuth (interactive): apify login (opens browser)
2. Environment variable: export APIFY_TOKEN=your_token_here
3. From .env file: source .env (if the file contains APIFY_TOKEN=...)

Generate token: https://console.apify.com/settings/integrations

Workflow

Step 0: Verify CLI readiness before doing anything else

Before using the Apify CLI, always verify the local environment:

1. Check that the CLI is installed:

    apify --help

If this fails, install the CLI first:

       npm install -g apify-cli

2. Check that the CLI is authenticated:

    # Auth check — do NOT pipe to /dev/null, you need to see errors
    apify info 2>&1

If this shows the user is not logged in, instruct them to authenticate with a token:

    apify login --token TOKEN

3. Run Apify CLI commands with all permissions when needed by the agent sandbox.

4. Assume many Apify commands block with zero output until completion. For blocking runs, set block_until_ms to at least 60000.

5. For long or unknown-duration runs, prefer the async pattern:

    apify actors start "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

Then poll the run status:

    apify info actor-runs/RUN_ID --json

Check .status for SUCCEEDED or FAILED.

Step 1: Understand goal and select Actor

Identify the target platform and use case. Read references/actor-index.md to find the right Actor. Prefer apify-tier actors; use community-tier only when no apify actor covers the task. For input schemas, fetch dynamically: apify actors info "ACTOR_ID" --input --json 2>/dev/null If the output is empty, re-run without the redirect (2>&1) to surface auth or network errors before proceeding.

If the task involves a multi-step pipeline, also read the matching workflow guide:

If no Actor matches in the index, search dynamically:

apify actors search "KEYWORDS" --json --limit 10 2>/dev/null

From results: items[].username/items[].name (Actor ID), items[].title, items[].stats.totalUsers30Days, items[].currentPricingInfo.pricingModel.

Step 2: Fetch Actor schema and check gotchas

Some Actors don't register an input schema with the platform (their schema lives in code). Try schema sources in this order — fall through on empty/error:

1. Input schema (human-readable):

    apify actors info "ACTOR_ID" --input 2>/dev/null

If output is Error: No input schema found for this Actor, skip to source 2.

2. Input schema (JSON keys only):

    apify actors info "ACTOR_ID" --input --json 2>/dev/null | jq '.input.schema.properties // empty | keys'

Empty result means no registered schema — fall through to source 3. To drill into a specific field:

    apify actors info "ACTOR_ID" --input --json 2>/dev/null | jq '.input.schema.properties.FIELD_NAME'

3. README fallback (always works, contains usage examples):

    apify actors info "ACTOR_ID" --readme 2>/dev/null

Grep the README for an "Input" / "Example input" section to copy the JSON shape.

4. Last resort — call with minimal known input (e.g. {"startUrls":[{"url":"..."}]} for crawlers) and let the Actor surface validation errors that reveal required fields. See references/gotchas.md for known-good minimal inputs for common Actors.

Also read references/gotchas.md to check for common pitfalls and cost guardrails for the selected Actor.

Step 3: Configure and run

Skip user preferences for simple lookups (e.g., "Nike's follower count"). Go straight to running with quick answer mode.

For larger tasks, confirm output format (quick answer / CSV / JSON) and result count.

Before starting the run, double-check whether the task is short enough for a blocking call or should use the async pattern from Step 0.

Standard run (blocking):

    apify actors call "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

From output: .id (run ID), .status, .defaultDatasetId, .stats.durationMillis

Fetch results:

    apify datasets get-items DATASET_ID --format json

For CSV: apify datasets get-items DATASET_ID --format csv

Quick answer mode: Fetch results as JSON, pick top 5, present formatted in chat.

Save to file: Fetch results, use Write tool to save as YYYY-MM-DD_descriptive-name.csv or .json.

Large/long-running scrapes:

    apify actors start "ACTOR_ID" -i 'JSON_INPUT' --json 2>/dev/null

Poll: apify info actor-runs/RUN_ID --json (check .status for SUCCEEDED or FAILED).

Step 4: Deliver results

Report: result count, file location (if saved), key data fields, and links:

• Dataset: https://console.apify.com/storage/datasets/DATASET_ID
• Run: https://console.apify.com/actors/runs/RUN_ID

For multi-step workflows: suggest the next pipeline step from the workflow guide.

Troubleshooting

Common errors and pitfalls are documented in references/gotchas.md. Read it before running PPE (pay-per-event) Actors.