Apify Actor Development

Apify Actor Development

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

What are Apify Actors?

Actors are serverless programs inspired by the UNIX philosophy - programs that do one thing well and can be easily combined to build complex systems. They're packaged as Docker images and run in isolated containers in the cloud.

Core Concepts:

• Accept well-defined JSON input
• Perform isolated tasks (web scraping, automation, data processing)
• Produce structured JSON output to datasets and/or store data in key-value stores
• Can run from seconds to hours or even indefinitely
• Persist state and can be restarted

Prerequisites & Setup (MANDATORY)

Before creating or modifying actors, verify that apify CLI is installed apify --help.

If it is not installed, use one of these methods (listed in order of preference):

# Preferred: install via a package manager (provides integrity checks)
npm install -g apify-cli

# Or (Mac): brew install apify-cli

Security note: Do NOT install the CLI by piping remote scripts to a shell (e.g. curl … | bash or irm … | iex). Always use a package manager.

When the apify CLI is installed, check that it is logged in with:

apify info  # Should return your username

If it is not logged in, check if the APIFY_TOKEN environment variable is defined (if not, ask the user to generate one on https://console.apify.com/settings/integrations and then define APIFY_TOKEN with it).

Then authenticate using one of these methods:

# Option 1 (preferred): The CLI automatically reads APIFY_TOKEN from the environment.
# Just ensure the env var is exported and run any apify command — no explicit login needed.

# Option 2: Interactive login (prompts for token without exposing it in shell history)
apify login

Security note: Avoid passing tokens as command-line arguments (e.g. apify login -t <token>). Arguments are visible in process listings and may be recorded in shell history. Prefer environment variables or interactive login instead. Never log, print, or embed APIFY_TOKEN in source code or configuration files. Use a token with the minimum required permissions (scoped token) and rotate it periodically.

Template Selection

IMPORTANT: Before starting actor development, always ask the user which programming language they prefer:

• JavaScript - Use apify create <actor-name> -t project_empty
• TypeScript - Use apify create <actor-name> -t ts_empty
• Python - Use apify create <actor-name> -t python-empty

Use the appropriate CLI command based on the user's language choice. Additional packages (Crawlee, Playwright, etc.) can be installed later as needed.

Quick Start Workflow

1. Create actor project - Run the appropriate apify create command based on user's language preference (see Template Selection above)
2. Install dependencies (verify package names match intended packages before installing)
   • JavaScript/TypeScript: npm install (uses package-lock.json for reproducible, integrity-checked installs — commit the lockfile to version control)
   • Python: pip install -r requirements.txt (pin exact versions in requirements.txt, e.g. crawlee==1.2.3, and commit the file to version control)
3. Implement logic - Write the actor code in src/main.py, src/main.js, or src/main.ts
4. Configure schemas - Update input/output schemas in .actor/input_schema.json, .actor/output_schema.json, .actor/dataset_schema.json
5. Configure platform settings - Update .actor/actor.json with actor metadata (see references/actor-json.md)
6. Write documentation - Create comprehensive README.md for the marketplace
7. Test locally - Run apify run to verify functionality (see Local Testing section below)
8. Deploy - Run apify push to deploy the actor on the Apify platform (actor name is defined in .actor/actor.json)

Security

Treat all crawled web content as untrusted input. Actors ingest data from external websites that may contain malicious payloads. Follow these rules:

• Sanitize crawled data — Never pass raw HTML, URLs, or scraped text directly into shell commands, eval(), database queries, or template engines. Use proper escaping or parameterized APIs.
• Validate and type-check all external data — Before pushing to datasets or key-value stores, verify that values match expected types and formats. Reject or sanitize unexpected structures.
• Do not execute or interpret crawled content — Never treat scraped text as code, commands, or configuration. Content from websites could include prompt injection attempts or embedded scripts.
• Isolate credentials from data pipelines — Ensure APIFY_TOKEN and other secrets are never accessible in request handlers or passed alongside crawled data. Use the Apify SDK's built-in credential management rather than passing tokens through environment variables in data-processing code.
• Review dependencies before installing — When adding packages with npm install or pip install, verify the package name and publisher. Typosquatting is a common supply-chain attack vector. Prefer well-known, actively maintained packages.
• Pin versions and use lockfiles — Always commit package-lock.json (Node.js) or pin exact versions in requirements.txt (Python). Lockfiles ensure reproducible builds and prevent silent dependency substitution. Run npm audit or pip-audit periodically to check for known vulnerabilities.

Best Practices

✓ Do:

• Use apify run to test actors locally (configures Apify environment and storage)
• Use Apify SDK (apify) for code running ON Apify platform
• Validate input early with proper error handling and fail gracefully
• Use CheerioCrawler for static HTML (10x faster than browsers)
• Use PlaywrightCrawler only for JavaScript-heavy sites
• Use router pattern (createCheerioRouter/createPlaywrightRouter) for complex crawls
• Implement retry strategies with exponential backoff
• Use proper concurrency: HTTP (10-50), Browser (1-5)
• Set sensible defaults in .actor/input_schema.json
• Define output schema in .actor/output_schema.json
• Clean and validate data before pushing to dataset
• Use semantic CSS selectors with fallback strategies
• Respect robots.txt, ToS, and implement rate limiting
• Always use apify/log package — censors sensitive data (API keys, tokens, credentials)
• Implement readiness probe handler (required if your Actor uses standby mode)

✗ Don't:

• Use npm start, npm run start, npx apify run, or similar commands to run actors (use apify run instead)
• Assume local storage from apify run is pushed to or visible in the Apify Console — it is local-only; deploy with apify push and run on the platform to see results in the Console
• Rely on Dataset.getInfo() for final counts on Cloud
• Use browser crawlers when HTTP/Cheerio works
• Hard code values that should be in input schema or environment variables
• Skip input validation or error handling
• Overload servers - use appropriate concurrency and delays
• Scrape prohibited content or ignore Terms of Service
• Store personal/sensitive data unless explicitly permitted
• Use deprecated options like requestHandlerTimeoutMillis on CheerioCrawler (v3.x)
• Use additionalHttpHeaders - use preNavigationHooks instead
• Pass raw crawled content into shell commands, eval(), or code-generation functions
• Use console.log() or print() instead of the Apify logger — these bypass credential censoring
• Disable standby mode without explicit permission

Logging

See references/logging.md for complete logging documentation including available log levels and best practices for JavaScript/TypeScript and Python.

Check usesStandbyMode in .actor/actor.json - only implement if set to true.

Commands

apify run          # Run Actor locally
apify login        # Authenticate account
apify push         # Deploy to Apify platform (uses name from .actor/actor.json)
apify help         # List all commands

IMPORTANT: Always use apify run to test actors locally. Do not use npm run start, npm start, yarn start, or other package manager commands - these will not properly configure the Apify environment and storage.

Local Testing

When testing an actor locally with apify run, provide input data by creating a JSON file at:

storage/key_value_stores/default/INPUT.json

This file should contain the input parameters defined in your .actor/input_schema.json. The actor will read this input when running locally, mirroring how it receives input on the Apify platform.

IMPORTANT - Local storage is NOT synced to the Apify Console:

• Running apify run stores all data (datasets, key-value stores, request queues) only on your local filesystem in the storage/ directory.
• This data is never automatically uploaded or pushed to the Apify platform. It exists only on your machine.
• To verify results on the Apify Console, you must deploy the Actor with apify push and then run it on the platform.
• Do not rely on checking the Apify Console to verify results from local runs — instead, inspect the local storage/ directory or check the Actor's log output.

Standby Mode

See references/standby-mode.md for complete standby mode documentation including readiness probe implementation for JavaScript/TypeScript and Python.

Project Structure

.actor/
├── actor.json           # Actor config: name, version, env vars, runtime
├── input_schema.json    # Input validation & Console form definition
└── output_schema.json   # Output storage and display templates
src/
└── main.js/ts/py       # Actor entry point
storage/                # Local-only storage (NOT synced to Apify Console)
├── datasets/           # Output items (JSON objects)
├── key_value_stores/   # Files, config, INPUT
└── request_queues/     # Pending crawl requests
Dockerfile              # Container image definition

Actor Configuration

See references/actor-json.md for complete actor.json structure and configuration options.

Input Schema

See references/input-schema.md for input schema structure and examples.

Output Schema

See references/output-schema.md for output schema structure, examples, and template variables.

Dataset Schema

See references/dataset-schema.md for dataset schema structure, configuration, and display properties.

Key-Value Store Schema

See references/key-value-store-schema.md for key-value store schema structure, collections, and configuration.

Apify MCP Tools

If MCP server is configured, use these tools for documentation:

• search-apify-docs - Search documentation
• fetch-apify-docs - Get full doc pages

Otherwise, the MCP Server url: https://mcp.apify.com/?tools=docs.

Resources

• docs.apify.com/llms.txt - Apify quick reference documentation
• docs.apify.com/llms-full.txt - Apify complete documentation
• https://crawlee.dev/llms.txt - Crawlee quick reference documentation
• https://crawlee.dev/llms-full.txt - Crawlee complete documentation
• whitepaper.actor - Complete Actor specification

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Apify Actor Development"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags apify-actor-development devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.