5 Apify Actors for Dify Workflows Firecrawl Can't Handle

The problem: you're building a workflow in Dify and reach for a scraping tool. Firecrawl, Tavily, Jina, SerpApi — fine for "scrape this URL" or "search the web." But the moment you need podcast metadata with host emails, historical snapshots from a specific date, scored GitHub repos, verified contacts off a website, or tech-stack fingerprints with CVEs, those tools either return a wall of HTML the LLM has to re-parse, or they don't go to that source at all.

This post is a drop-in reference for Dify users who hit that wall. Five Apify actors, the actor ID you paste into the Apify plugin's Run Actor node, and the minimal input JSON for each. Same plugin you may already have installed (or can install in 30 seconds from the Dify marketplace).

What is the Apify plugin for Dify? A meta-dispatcher node in Dify that takes an actor ID and an input JSON, runs the actor on Apify, and returns the dataset to the next node. You pick the actor. Why it matters: Dify ships with bundled scrapers (Firecrawl, Tavily, Jina) that hit the public web. The Apify plugin opens a side door to 6,000+ specialised actors that hit specific platforms — podcasts, archives, GitHub, lead intelligence, tech fingerprinting. Use it when: the data you need lives on a specific platform (Apple Podcasts, Internet Archive, GitHub) or in a structured shape (verified emails, CVEs, ranked repos) that a generic crawler doesn't return.

Quick answer

• What this post gives you: 5 actor IDs + sample input JSON ready to paste into the Apify plugin's Run Actor node in Dify.
• When to use this pattern: known platform target. Dify's bundled Firecrawl, Tavily, Jina, SerpApi handle arbitrary URLs and web search. They don't handle "give me the host email for every B2B SaaS podcast that published this month."
• When NOT to use this pattern: you only need to fetch the markdown of one URL. Stick with Firecrawl.
• Typical setup: install the Apify plugin in Dify, paste an Apify API token once, then drop a Run Actor node anywhere in a workflow with an actor ID and input JSON.
• Main tradeoff: the Apify plugin is a meta-dispatcher — you have to know which actor to call. This post is the cheat sheet for five high-value cases.

In this article: How the plugin works · Why generic crawlers fall over · The 5 actors · When to use Apify vs Firecrawl · Best practices · FAQ

Key takeaways

• Dify's Apify plugin has roughly 300 installs as of April 2026 — small, but it unlocks the entire 6,000+ actor catalogue, dwarfing the bundled scrapers' coverage.
• Generic crawlers (Firecrawl 94k installs, Tavily 191k, Jina 84k) work for arbitrary URLs and search. They don't return structured podcast metadata, classified GitHub trajectories, archived snapshots with diffs, verified emails, or CVE-flagged tech stacks.
• Five actors covered in this post handle five concrete jobs Firecrawl can't: podcast directory scraping ($0.05/podcast), Wayback Machine analysis ($0.001/snapshot), GitHub repo intelligence ($0.15/repo), website lead intelligence ($0.15/site), and tech stack detection ($0.35/site).
• Every example below includes a copy-pasteable input JSON sourced from the actor's input_schema.json defaults — drop straight into Dify's Run Actor node.
• All five are pay-per-event, so you only pay for results, not for failed runs or skipped rows.

Compact examples

What is the Apify plugin for Dify?

Definition (short version): the Apify plugin for Dify is a meta-dispatcher tool node that runs any Apify actor by ID and returns its dataset to the next step in a workflow.

The plugin's UI is intentionally thin. It exposes one main capability — Run Actor — that takes a free-text actor ID and a JSON input, kicks off a run on Apify's platform, and pipes the resulting dataset into the next node. There are also lower-level capabilities for dataset listing and key-value-store reads, used less often.

This is a deliberate design choice. Apify has 6,000+ actors covering every platform you'd want to scrape. Bundling them as individual Dify tools would be unworkable. The tradeoff is that the plugin doesn't surface actors in Dify's tool picker — the user has to bring an actor ID. Hence this post.

Also known as: Dify Apify integration, Apify Run Actor node, Apify connector for Dify, dify scraping tools, dify plugin for web data extraction, dify long-tail scraping.

How the Apify plugin for Dify works

The flow is short. Install the Apify plugin from Dify's plugin marketplace. Paste an Apify API token once at the workspace level. Inside any workflow, drop a Run Actor node. Set two things: the actor ID (in username/actor-name format) and a JSON input matching that actor's input schema.

When the workflow fires, the plugin posts the input to Apify's API, polls until the run finishes, then returns the dataset items as a JSON array your next node can iterate over. For long-running actors, you can wire an Apify webhook to fire a Dify endpoint instead, decoupling the run from the workflow's request lifecycle.

The dataset comes back structured — not raw HTML. That's the actual win over generic crawlers. A podcast actor returns {title, ownerEmail, episodeFrequency}, not a 40 KB HTML blob the LLM has to re-extract from. Less tokens, more determinism, fewer hallucinations downstream.

Why do generic crawlers fail on platform-specific data?

Firecrawl, Tavily, and Jina are good at one thing: take a URL or a query, return clean markdown or text. They're general-purpose. They don't know that Apple Podcasts has a search API, or that podcast host emails live in the itunes:owner tag inside RSS feeds, or that the Internet Archive's CDX API serves snapshot lists, or that GitHub's search API caps at 1,000 results unless you partition by star ranges.

If you point Firecrawl at podcasts.apple.com and ask for B2B SaaS shows, you get whatever the page renders — titles, descriptions, no host emails (the public UI never shows them), no episode frequency, no active-status flag. To get useful structured data, your agent has to query the iTunes Search API, fetch hundreds of RSS feeds in parallel, parse XML, normalise titles for cross-platform deduplication, calculate publishing cadence from publish dates — and that's just one of the five jobs in this post.

Each platform has its own access pattern, rate limits, encoding quirks, and edge cases. A generic crawler doesn't know any of that. A platform-specific actor encodes all of it once and exposes a clean input/output contract.

What the bundled Dify tools handle vs what they don't

Plugin install counts and feature scope based on publicly available information as of April 2026 and may change.

1. Podcast Directory Scraper — podcasts with host emails

Job: searches Apple Podcasts and Spotify by keyword, fetches each show's RSS feed, and returns 20+ structured fields per podcast — including ownerEmail extracted from the itunes:owner tag, publishing frequency, and active status.

Why Firecrawl can't deliver this: Apple Podcasts' public web UI never shows host emails. They live inside RSS feeds, which themselves aren't visible from the Apple Podcasts site or app. A generic crawler pointed at a podcast page returns title, description, episode list — no email, no frequency, no active flag. The Apify actor automates the whole pipeline: iTunes Search API → RSS fetch → itunes:owner parse → frequency calc → active-status flag.

Dify use case: RAG pipeline that ingests podcast metadata for a "find me a host to pitch" agent. Or an outbound-pitch workflow that calls this actor weekly, drops new shows into a Dify dataset, and routes them to an outreach LLM that drafts personalised pitch emails using the show's description and recent episode titles.

Dify Run Actor node config:

• Actor ID: ryanclinton/podcast-directory-scraper
• Input JSON:

{
    "searchTerms": ["B2B SaaS marketing", "sales enablement"],
    "maxResults": 50,
    "country": "us",
    "activeOnly": true,
    "includeEpisodes": false
}

Pricing: $0.05 per podcast. A 50-podcast outreach run is $2.50.

Links: Podcast Directory Scraper on ApifyForge · Podcast Directory Scraper on Apify Store

2. Wayback Machine Search — historical snapshots with change intelligence

Job: queries the Internet Archive's CDX API for any URL or domain, detects changes between consecutive snapshots, classifies each by category (pricing / legal / product / layout / navigation / copy / contact) and magnitude (minor / moderate / major), and emits a tamper-evident SHA-256 hash chain across events.

Why Firecrawl can't deliver this: Firecrawl scrapes the live web. The Wayback Machine is a separate dataset accessed via the CDX API, which has its own pagination, 10,000-row cap, and filtering syntax. The Apify actor handles all that, plus the change-detection layer, classification, magnitude scoring, closest-date lookup, multi-URL comparison, and audit-evidence chain. Closest-date lookup ("what did the page say on 2024-06-15?") in particular is impossible without an archive — that's the whole point.

Dify use case: compliance and legal evidence agents. "Did this competitor change their pricing in the last 6 months?" "What did our terms of service say on the date this customer signed up?" The actor returns a single snapshot record with distanceFromTargetDays plus a tamper-evident chainHash you can include in a memo.

Other Dify use case: a competitor-watch agent that runs weekly with monitor: true, fires only on moderate or major pricing/product changes, and posts to Slack via Dify's webhook node.

Dify Run Actor node config (closest-date evidence):

• Actor ID: ryanclinton/wayback-machine-search
• Input JSON:

{
    "url": "stripe.com/legal/restricted-businesses",
    "targetDate": "2024-06-15",
    "matchType": "exact",
    "includeContent": true
}

Dify Run Actor node config (scheduled competitor watch):

{
    "urls": ["competitor-a.com/pricing", "competitor-b.com/pricing"],
    "monitor": true,
    "alertOnMagnitude": "moderate",
    "useCase": "competitor"
}

Pricing: $0.001 per snapshot. A 1,000-snapshot historical pull is $1.

Links: Wayback Machine Search on ApifyForge · Wayback Machine Search on Apify Store

3. GitHub Repo Intelligence — scored, classified repos

Job: queries GitHub's search API, enriches each repo with community profile, activity stats, and contributor data, then computes 5 composite scores (health, adoption, community, risk, outreach) plus a lifecycle classification (GROWING / STABLE / DECLINING / COLLAPSING / REVIVING) and a decision verdict (STRONGLY_RECOMMENDED → HIGH_RISK).

Why Firecrawl can't deliver this: Firecrawl pointed at github.com/search returns the rendered HTML of a paginated UI. To get scored, classified data at the scale a workflow needs, you'd own the search-API integration, the 1,000-result cap workaround (auto-partition by star range), the community-profile and activity enrichment calls, the scoring weights, the lifecycle classifier, the change detection vs the previous run, the contributor-email extraction with bot-filter, and the percentile benchmarks. That's a maintained intelligence service, not a script.

Dify use case: "Is this dependency safe to adopt?" agent. The workflow takes a repo name, calls this actor with compareRepos, and the LLM gets back a verdict + risk level + percentile benchmark. Or a weekly trend-watch agent for an internal tooling team — mode: "trend-watch" returns rising repos in a category with breakout detection.

Dify Run Actor node config (adopt/avoid decision):

• Actor ID: ryanclinton/github-repo-search
• Input JSON:

{
    "compareRepos": ["facebook/react", "vuejs/vue", "sveltejs/svelte"],
    "enrichRepoData": true,
    "mode": "adoption-shortlist"
}

Dify Run Actor node config (category market map):

{
    "query": "machine learning language:python",
    "mode": "market-map",
    "maxResults": 100,
    "minStars": 500,
    "excludeArchived": true
}

Pricing: $0.15 per repo. A 30-repo comparison costs $4.50.

Links: GitHub Repo Search on ApifyForge · GitHub Repo Search on Apify Store

4. Website Lead Intelligence — verified contacts from a domain list

Job: takes a list of company domains, crawls each one, extracts decision-makers and emails, verifies deliverability, and returns a send-ready record per domain with a clear next action — SEND_NOW, VERIFY_FIRST, SKIP, or ENRICH_MORE.

Why Firecrawl can't deliver this: Firecrawl returns markdown of one page at a time. To turn a list of domains into a send-ready outreach list, you'd own multi-page crawling per domain (contact pages, team pages, imprint pages, hidden EU legal pages), email extraction with role-vs-personal classification, MX-record verification, catch-all domain detection, decision-maker title parsing, lead scoring, send-decision logic, and CRM export formatting. That's an outbound infrastructure stack, not a scraper.

Dify use case: outbound lead-gen agent. Pipeline takes a Google Sheet of target domains, runs them through this actor with goal: "high-deliverability", filters to SEND_NOW records, and feeds each one into an LLM personaliser that drafts a first-touch email using the lead's role, the company's likely buying angle, and the surfaced opening-line stem.

Dify Run Actor node config:

• Actor ID: ryanclinton/website-contact-scraper
• Input JSON:

{
    "urls": ["https://stripe.com", "https://shopify.com"],
    "goal": "high-deliverability",
    "autoFilter": "send-now-only",
    "exportFormats": ["instantly"]
}

Pricing: $0.15 per website with contact data. Filtered or empty domains are not charged. A 100-domain run typically returns 40–60 usable leads at roughly $9–15 per run.

Links: Website Contact Scraper on ApifyForge · Website Contact Scraper on Apify Store

5. Website Tech Stack Detector — fingerprint, CVEs, security grade

Job: detects 100+ web technologies on a domain, flags known CVEs against detected versions, grades security headers OWASP-style A–F, classifies stack changes vs the prior run (CDN swap, CMS migration, framework rewrite, payment replatform), and emits prioritised remediation actions.

Why Firecrawl can't deliver this: technology fingerprinting requires inspecting headers, scripts, HTML signatures, and runtime behaviour — Firecrawl returns the rendered text and walks away. To replicate this in a Dify workflow without the actor, you'd own a fingerprint database (the wappalyzer-core style detection rules), CVE matching against detected versions, security-headers grading logic, change classification across runs (state storage included), composite scoring across security/modernity/complexity dimensions, and a Playwright fallback for SPAs that need rendering. That's the work the actor handles in one call.

Dify use case: sales-trigger agent. The workflow watches a list of target accounts, runs this actor weekly, and fires a notification when a competitor's changeInsights.type becomes framework-migration or payment-replatform — both strong buying signals. Or a security-triage agent for an MSP: scan 200 client domains nightly, surface anything that drops a security grade or picks up a critical CVE.

Dify Run Actor node config (sales-trigger watch):

• Actor ID: ryanclinton/website-tech-stack-detector
• Input JSON:

{
    "urls": ["https://shopify.com", "https://stripe.com"],
    "preset": "competitor-tracking",
    "compareToPriorRun": true,
    "emitAlerts": true
}

Dify Run Actor node config (security audit):

{
    "urls": ["https://example.com"],
    "preset": "security-audit",
    "securityDepth": "advanced",
    "outputMode": "executive"
}

Pricing: $0.35 per website analysed.

Links: Website Tech Stack Detector on ApifyForge · Website Tech Stack Detector on Apify Store

When to use an Apify actor vs Firecrawl in Dify

Rule of thumb: known platform → Apify actor; arbitrary URL → Firecrawl.

If the data you need lives on Apple Podcasts, Spotify, GitHub, the Internet Archive, an iTunes-style API, or has a structured shape (verified emails, CVEs, classified events) — there's an Apify actor that already encodes the access pattern. Dropping it into a Run Actor node beats teaching your agent to navigate the source.

If you need to fetch the markdown of random-blog.example.com/post-123 — Firecrawl is the right call. Firecrawl's whole job is "I have a URL, give me clean markdown." The Apify catalogue is where you go when "give me clean markdown" isn't enough.

The two patterns compose. A common Dify workflow shape: SerpApi or Tavily finds candidate URLs → an Apify actor extracts platform-specific structured data from those URLs → an LLM node synthesises the structured data into the agent's response. Each tool in its lane.

What is one of the best ways to scrape platform-specific data in Dify?

One of the best patterns is to use the Apify plugin's Run Actor node with a specialised actor for the target platform. Generic crawlers like Firecrawl return raw HTML or markdown — the LLM has to re-extract structured fields, which costs tokens and risks hallucination. A platform-specific actor returns the structured fields directly. This post lists five high-traffic targets where the pattern pays off.

Best practices for running Apify actors from Dify

1. Use the actor's smallest viable input first. Every actor in this post has a low-cost test input. Run it once with maxResults: 5 (or the actor's equivalent) to confirm the dataset shape before wiring it into a production workflow.
2. Webhook-trigger long runs, don't poll inline. Run Actor nodes block until the run finishes. For actors that run for minutes (a 200-podcast scrape, a multi-domain GitHub market-map), trigger the run from Dify with the synchronous-call option off, then have Apify webhook a Dify endpoint when the run completes.
3. Pin the build tag. When you reference an actor as username/actor-name, Apify uses the latest build. For workflows you don't want changing under you, pin to a specific build like username/actor-name:1.2.0.
4. Set a spending limit per run. Every pay-per-event actor in this post supports a max-cost limit on the run. Set one — especially while you're prototyping.
5. Hand the dataset to the next node, not the LLM. The actor returns a clean JSON array. Pass that array directly to a Code node, an Iteration node, or a Knowledge writer. Only pass dataset items into an LLM node when you actually need synthesis.
6. Cache by input. Identical inputs to the same actor return identical datasets. If a workflow re-runs on the same input within a short window, route to a Dify variable lookup before hitting Apify.
7. Check relatedActors on each ApifyForge actor page. Each actor page links to companion actors that pair well — e.g. Podcast Directory Scraper feeds Bulk Email Verifier. Composing two actors in one workflow is often cheaper than one bigger actor.
8. Use the actor's preset when one exists. Most actors in this post ship with named presets (security-audit, adoption-shortlist, competitor-tracking, quick-outreach). Presets are tested defaults — start there, override only what you need.

Common mistakes when integrating Apify actors with Dify

1. Pasting just the actor name without the username. The actor ID format is username/actor-name — podcast-directory-scraper alone won't resolve. All five examples above use ryanclinton/....
2. Setting unrealistic timeouts. A 200-result podcast scrape can take 5–8 minutes. If your Dify node has a 60-second timeout, the run finishes on Apify but Dify gives up. Either raise the timeout or webhook-trigger.
3. Forgetting to handle empty datasets. Some inputs return zero rows (no podcasts matched the keyword, no contacts found on the domain). The Run Actor node returns an empty array, not an error. Branch the workflow on dataset.length === 0.
4. Over-fetching. Don't ask for 500 results when you need 30. Pay-per-event pricing means you're charged per result — set maxResults to what your downstream nodes actually use.
5. Confusing Apify's plugin scope with the Apify SDK. The Dify plugin can run actors and read datasets. It cannot trigger builds, modify actor source, or access billing. That's intentional and matches the Apify scoped-token model.

How to compose two Apify actors in a single Dify workflow

A common shape: one actor produces leads, a second actor enriches them. Example: Podcast Directory Scraper returns shows with some ownerEmail fields populated and some null. For shows with websiteUrl but no ownerEmail, route the URL list through Website Lead Intelligence in a second Run Actor node. Final output: maximum email coverage at minimum cost.

Same pattern for the GitHub case: GitHub Repo Search with mode: "market-map" returns 100 ranked repos. Filter to the top 10 by adoptionVerdict, then run those through mode: "repo-due-diligence" for full intelligence. Two actors, two costs, one workflow.

Mini case study: Dify RAG pipeline for a B2B podcast outreach agent

Before: the team's first Dify build used Firecrawl pointed at podcasts.apple.com/us/genre/podcasts-business. The crawler returned chart-list HTML — show titles and Apple Podcasts URLs, no host emails, no frequency, no active flag. The agent then had to call Firecrawl a second time per show to fetch the RSS feed (when it could find one), parse XML, and pull itunes:owner. End-to-end time per show: 8–12 seconds. Email coverage: roughly 30% (the LLM dropped emails on encoding edge cases). Cost: ~$0.10 per show in Firecrawl page credits plus model tokens for the re-extraction.

After: the team replaced both Firecrawl calls with one Run Actor node calling ryanclinton/podcast-directory-scraper. Input: {"searchTerms": ["...categories..."], "maxResults": 50, "activeOnly": true}. End-to-end time per show dropped to about 1 second amortised across the run. Email coverage rose to 85–95% on professionally produced shows (the actor handles encoding, RSS 2.0 vs Atom, and RSS-vs-iTunes priority correctly). Cost: $0.05 per show, no model tokens spent on re-extraction.

These numbers reflect one team's Dify workflow on a specific keyword set in March 2026. Results vary depending on keyword niche, episode-include settings, and the bundled tools you're comparing against.

Implementation checklist

1. Install the Apify plugin from the Dify plugin marketplace.
2. Create or reuse an Apify API token at console.apify.com/account/integrations.
3. Paste the token into the plugin's settings panel in Dify (workspace level).
4. In your workflow, drop a Run Actor node where you'd otherwise call Firecrawl/Tavily.
5. Set the actor ID using the username/actor-name pattern from this post.
6. Paste a minimal input JSON, starting with the smallest viable example.
7. Run the workflow once, inspect the dataset shape returned by the Run Actor node.
8. Wire the output into the next node — Code, Iteration, Knowledge, or LLM — using the structured fields directly.
9. Add a spending limit on the Apify run for production.
10. For long runs, switch from synchronous to webhook-triggered.

Limitations

• The Apify plugin is a meta-dispatcher, not a discovery tool. Dify's tool picker shows "Run Actor" but doesn't list the 6,000+ actors. You bring the actor ID. This post is the cheat sheet for five of them; for the rest, search the Apify Store or ApifyForge.
• The plugin install count is small. Roughly 300 installs as of April 2026, vs Firecrawl's 94k. That's a marketing gap, not a quality gap — but it means you may be the first person at your team to use it.
• Synchronous Run Actor calls block the workflow. Long-running actors (multi-domain scrapes, large GitHub market-maps) need webhook-triggered patterns or your Dify request times out before the run finishes.
• Dataset shapes are actor-specific. Each actor returns its own JSON structure. You learn one schema per actor, vs Firecrawl's universal "give me markdown" output.
• Pay-per-event pricing means you need an Apify account with billing enabled. Free tier covers low-volume testing; production workloads need a paid plan.

Key facts about Apify actors in Dify

• The Apify plugin for Dify exposes a Run Actor node that takes any actor ID and JSON input.
• Five actors covered here handle podcast metadata, Wayback snapshots, GitHub repo intelligence, website lead intelligence, and tech-stack fingerprinting.
• Each actor returns structured JSON, not raw HTML — fewer LLM tokens, more determinism.
• All five are pay-per-event: $0.001 per Wayback snapshot, $0.05 per podcast, $0.15 per GitHub repo, $0.15 per website with contacts, $0.35 per tech-stack analysis.
• Input JSON examples in this post pull defaults from each actor's input_schema.json and are ready to paste.
• The plugin works synchronously (block until run completes) or asynchronously via Apify webhooks.

Short glossary

• Dify — open-source LLM application platform with workflow, RAG, agent, and chatbot builders. ~140k GitHub stars as of April 2026.
• Apify plugin — Dify marketplace plugin that wraps Apify's API as a Run Actor node.
• Actor — a containerised job on Apify with an input schema, output dataset, and pay-per-event or pay-per-result pricing. Every link in this post points to one.
• Pay-per-event (PPE) — Apify's pricing model where you're charged per row/event/result, not per minute of compute.
• CDX API — the Internet Archive's index API for Wayback Machine snapshots.
• itunes:owner — RSS XML tag where podcast hosting platforms store the creator's contact email.

Broader applicability

These patterns apply beyond Dify to any LLM workflow tool that supports HTTP-triggered tools:

• n8n — Apify has a native node; same username/actor-name pattern.
• Make — Apify modules wrap the same API.
• Zapier — Apify Zaps for run/poll/webhook triggers.
• LangChain / LlamaIndex — Apify provides loaders.
• Custom agents — any tool-calling LLM (OpenAI function-calling, Anthropic tool-use) can call the Apify HTTP API directly.

The principle holds across all of them: when the data lives on a known platform, prefer a platform-specific actor over a general-purpose crawler.

When you need this

Reach for the Apify-actor-in-Dify pattern when:

• The data lives on a specific known platform (Apple Podcasts, Spotify, GitHub, Internet Archive, a domain you want fingerprinted).
• You need structured output, not raw markdown.
• The job has platform-specific quirks (rate limits, encoding, hidden APIs, pagination caps).
• You want to pay per result, not per compute minute.
• You want a maintained service, not a script your team has to babysit.

You probably don't need this if:

• You only need the markdown of one URL.
• The platform you're targeting doesn't have a maintained actor (search the Store first).
• Your workflow is internal and doesn't tolerate any third-party HTTP calls.
• You're prototyping and a 5-minute Firecrawl call is fine.

Frequently asked questions

How do I install the Apify plugin in Dify?

Open Dify, go to the plugin marketplace, search for "Apify", and click Install. After install, paste an Apify API token in the plugin's settings panel at the workspace level. The plugin appears as a tool category in the workflow builder; the Run Actor node is the main capability you'll use.

What's the difference between the Apify plugin and Firecrawl in Dify?

Firecrawl is a general-purpose crawler that takes a URL and returns markdown — good for arbitrary web pages. The Apify plugin is a meta-dispatcher that runs any Apify actor by ID, so you can call platform-specific scrapers for podcasts, GitHub, archives, contacts, tech stacks, and 6,000+ other targets. Firecrawl is broad. Apify is deep.

Can I use the Apify plugin for free?

Apify's free plan includes monthly platform credits that cover low-volume testing of all five actors in this post. Production workloads typically need a paid plan with auto-recharge enabled. Pay-per-event actors only charge when they return results, so you don't pay for failed runs.

How do I find the right Apify actor for my Dify workflow?

Browse the Apify Store for the full catalogue (6,000+ actors), or ApifyForge for ApifyForge's curated set (300+ actors plus 90+ MCP servers). Search by source platform name — "github", "podcasts", "wayback" — and check the actor's input schema and dataset schema before integrating.

Why doesn't Dify have a native podcast / GitHub / Wayback tool?

Dify ships with general-purpose tools (Firecrawl, Tavily, Jina, SerpApi) that cover the most common cases. Maintaining 6,000+ specialised scrapers is exactly what Apify exists for — Dify's plugin model lets the platform tap into Apify's catalogue without bundling every actor as a first-class tool.

Can I run multiple Apify actors in one Dify workflow?

Yes. Drop multiple Run Actor nodes in sequence or in parallel. A common pattern is one actor for discovery (Podcast Directory Scraper, GitHub Repo Search) and a second for enrichment (Website Lead Intelligence, Bulk Email Verifier). Pass dataset items between nodes using Dify's variable system or an Iteration node.

What happens if the Apify run fails?

The Run Actor node returns an error to the workflow. In Dify, wrap it with an error-branching pattern (try/catch-style) and either retry, skip, or fall back to a generic crawler. Pay-per-event actors don't charge for failed runs, so retrying is safe.

Is the Apify plugin the same as the Apify MCP server?

No. The Apify plugin is for Dify specifically — it wraps the Apify API as a Dify tool. The Apify MCP server is a separate Model Context Protocol server that exposes Apify actors to MCP-compatible AI agents (Claude Desktop, Cursor, the upcoming MCP ecosystem). Different protocol, same underlying actors.

Ryan Clinton operates 300+ Apify actors and builds developer tools at ApifyForge.

Last updated: April 2026

This guide focuses on Dify, but the same Run Actor + structured-input pattern applies broadly to n8n, Make, Zapier, LangChain, LlamaIndex, and any tool-calling LLM that can hit an HTTP API.