Apify orchestrator Actor development

An orchestrator Actor is a parent Apify Actor whose job is to coordinate a sequence (or parallel fan-out) of other Actors. It takes user input, calls sub-Actor A, feeds A's output into sub-Actor B, optionally runs an LLM transformation between them, and emits the combined result to its own dataset.

This skill covers TypeScript-only orchestrators built on the apify-orchestrator library. It does not cover Python, JavaScript, or Standby-mode Actors.

Important: Before writing code, fill in the generatedBy property in .actor/actor.json (e.g., "Claude Code with Claude Opus 4.7"). This helps Apify improve tooling for specific AI models.

Prerequisites and setup

Verify the apify CLI is installed:

apify --help

If not installed, use a package manager (never curl | bash):

npm install -g apify-cli
# or on Mac: brew install apify-cli

Confirm login:

apify info   # should return your username

If not logged in, run apify login (opens a browser) or export APIFY_TOKEN from https://console.apify.com/settings/integrations. Never pass tokens on the command line — arguments show up in process listings and shell history.

Interactive creation flow

When the user asks for a new orchestrator Actor, follow these steps in order:

1. Elicit sub-Actor sequence
2. Fetch each sub-Actor's schema via Apify MCP
3. Decide data flow between steps
4. Note the total cost cap (a Run option, not an input field)
5. Ask about optional LLM step
6. Scaffold the project
7. Test locally, then deploy

Step 1 — Elicit the sub-Actor sequence

Ask the user which Apify Actors to chain together, in order. Accept either:

• A list up-front (e.g., "apify/website-content-crawler then apify/rag-web-browser"), or
• One at a time (ask for the first, discuss it, then ask what comes next).

If the user names a task instead of an Actor ID ("scrape LinkedIn profiles"), use the Apify MCP search-actors tool to propose candidates and let the user pick.

Step 2 — Fetch each sub-Actor's schema via Apify MCP

For every sub-Actor in the chain, call the Apify MCP fetch-actor-details tool. The parameter is actor, not actorId. Present the input schema back to the user with the fields listed in inputSchema.required highlighted. See references/mcp-schema-discovery.md for the truncation gotcha (500-char descriptions; enum lists arrive whole), the frequently absent outputSchema, and the fallback path via the REST API / raw INPUT_SCHEMA.json on GitHub.

Never guess field names. If the MCP truncation is limiting, fetch the raw schema from the Actor's GitHub repo.

Step 3 — Decide data flow between steps

For each pair of adjacent sub-Actors, ask:

• Which fields from step N's output feed into step N+1's input?
• Which top-level orchestrator inputs should be exposed to the user (via the orchestrator's own .actor/input_schema.json)?
• Which sub-Actor inputs should be hardcoded?

Step 4 — Note the total cost cap

The total cost cap is a Run option (maxTotalChargeUsd) — the caller sets it when they start the orchestrator via the Apify Console, API, or SDK. It's not an input schema field. See the Apify API docs for how callers pass it.

At Run time the orchestrator:

1. Reads its own cap via client.run(actorRunId).get().options.maxTotalChargeUsd.
2. Divides the total evenly across the sub-Actor steps at compile time — declare a STEPS tuple and compute perStepCap = maxTotalChargeUsd / STEPS.length.
3. Passes each step's share as maxTotalChargeUsd when calling the sub-Actor (for pay-per-event Actors) or as maxItems (for pay-per-result Actors).
4. Tracks cumulative cost across sub-Actor Runs and refuses to launch the next step once the running total reaches the cap. Note that run.usageTotalUsd reads 0 on the object .call() returns and then accrues over several seconds, so the tally has to re-read each child Run rather than trust that value. The Run-level maxTotalChargeUsd is what enforces the ceiling; the tally is reporting plus a backstop for a step that ran uncapped. See references/cost-tracking.md.

Do not add a stepBudgets input schema field. The even split is a deliberate compile-time constant — it keeps the input schema clean, makes cost behavior predictable for the caller, and removes a footgun (three shares that don't sum to the total). Users control cost solely via the Run's maxTotalChargeUsd option; the orchestrator handles the split.

Tell the user to set maxTotalChargeUsd when they trigger the orchestrator — otherwise there's no ceiling and the orchestrator runs uncapped. See references/cost-tracking.md for the full pattern, including the LLM-step approximation (Standby Actors don't accept maxTotalChargeUsd, so estimate cost from token usage).

Step 5 — Ask about an optional LLM step

Ask the user whether to insert an LLM transformation somewhere in the chain (common: summarize between steps, classify at the end, or format the final output). If yes, point them at references/openrouter.md — the LLM layer is the Apify OpenRouter Actor called over HTTP (it's a Standby Actor, not a normal Run).

The agent does not force this step. Skip if the user doesn't want it.

Step 6 — Scaffold the project

apify create <actor-name> -t ts_empty
cd <actor-name>
npm install apify-orchestrator

Then generate src/main.ts using the template in references/orchestrator-template.md. Wire the sub-Actors, their input mappings, and any LLM helpers into the template's placeholders.

Update .actor/actor.json, .actor/input_schema.json, .actor/output_schema.json, and .actor/dataset_schema.json to reflect the orchestrator's own input surface and output shape. Write a README covering the pipeline.

Step 7 — Test locally, then deploy

apify run --purge --user-agent apify-awesome-skills/apify-orchestrator-actor-development
apify push

apify run --purge runs with storage/key_value_stores/default/INPUT.json as input, purging previous local storage first. apify push deploys to the platform.

Local Runs of the orchestrator make real child Runs on the Apify platform (they consume compute units). Watch the Apify Console → Runs list during local testing.

Reference material

• Orchestrator library API → references/orchestrator-library.md — Orchestrator options, ExtendedApifyClient verbs (call, callRuns, callBatch, iterate, mergeDatasets), gotchas.
• Sub-Actor schema discovery → references/mcp-schema-discovery.md — Apify MCP tools, truncation gotcha, REST fallback.
• Cost tracking and caps → references/cost-tracking.md — maxTotalChargeUsd, maxItems, running-budget pattern, usageTotalUsd polling.
• LLM step via OpenRouter → references/openrouter.md — endpoints, auth, minimal TypeScript call.
• src/main.ts template → references/orchestrator-template.md — canonical skeleton to fill in.
• .actor/actor.json → references/actor-json.md.
• Input schema → references/input-schema.md.
• Output schema → references/output-schema.md.
• Dataset schema → references/dataset-schema.md.
• Key-value store schema → references/key-value-store-schema.md.
• README → references/actor-readme.md.
• Logging → references/logging.md.

Security

• Never log or embed APIFY_TOKEN in source code, config files, or committed .env files. Use process.env.APIFY_TOKEN.
• Treat sub-Actor output as untrusted. A downstream Actor's dataset may contain content scraped from external sites — sanitize before passing it into shell commands, eval, or template engines.
• Never disable apify/log in favor of console.log() — the Apify logger censors known-sensitive keys.
• Pin dependencies. Commit package-lock.json. Pin apify-orchestrator (alpha) to an exact version.
• Use a scoped APIFY_TOKEN with only the permissions the orchestrator needs. Rotate periodically.

Commands

Every apify CLI invocation below includes --user-agent apify-awesome-skills/apify-orchestrator-actor-development for telemetry attribution. Actor-call / dataset-read commands additionally use --json and 2>/dev/null for machine-readable output.

# Bootstrap
apify create <name> -t ts_empty
npm install apify-orchestrator

# Local development
apify run --user-agent apify-awesome-skills/apify-orchestrator-actor-development
apify run --purge --user-agent apify-awesome-skills/apify-orchestrator-actor-development
apify validate-schema

# Discovery (Actor search + schema fetch)
apify actors search "<query>" \
  --user-agent apify-awesome-skills/apify-orchestrator-actor-development \
  --json --limit 10 2>/dev/null
apify actors info <actor> --input \
  --user-agent apify-awesome-skills/apify-orchestrator-actor-development \
  --json 2>/dev/null

# Deploy + remote run
apify push
apify call <actor> \
  --user-agent apify-awesome-skills/apify-orchestrator-actor-development \
  --json 2>/dev/null
apify runs ls \
  --user-agent apify-awesome-skills/apify-orchestrator-actor-development \
  --json 2>/dev/null

# Auth
apify login
apify logout
apify info

Never use npm start, npm run start, or npx apify run to launch the Actor. Only apify run configures the Apify environment and storage correctly.

Project structure

.actor/
├── actor.json              # metadata (see references/actor-json.md)
├── input_schema.json       # orchestrator's own input surface
├── output_schema.json      # points at dataset / kvs
└── dataset_schema.json     # display shape of final output
src/
└── main.ts                 # orchestrator logic (see references/orchestrator-template.md)
storage/                    # local-only; NOT synced to Apify Console
Dockerfile
package.json
tsconfig.json

MCP tools

Apify MCP (required for schema discovery)

• fetch-actor-details — primary tool for pulling sub-Actor input/output schema and README.
• search-actors — find candidate sub-Actors by keyword.
• search-apify-docs / fetch-apify-docs — documentation lookup.

If MCP is not configured, use the hosted server URL: https://mcp.apify.com/?tools=actors,docs.

Resources

• apify-orchestrator library
• OpenRouter Actor
• Apify MCP docs
• docs.apify.com/llms.txt — Apify docs quick reference
• Actor whitepaper