Over the past year, I’ve been working with four very different (and very opinionated) workflow tools: Windmill, n8n, Langflow, and Temporal.
Each has its own sweet spot, its quirks, and its “oh-no-why-did-I-choose-this” moments.
This post is my field guide to these four tools — what they do best, where they fall short, and how I mix them together into a pragmatic, production-ready workflow stack.
1. Meet the Four Players
Before we dive into comparisons, let’s introduce the cast.
🛠 Windmill — The Script Wizard
Windmill is your best friend when you want to glue things together quickly. It lets you write scripts in Python, TypeScript, Go, and even build small internal apps and expose them as APIs. It’s ridiculously easy to chain functions together, build internal tooling, and deploy lightweight automations.
But here’s the thing: Windmill is not built for high concurrency.
Every script spins up an isolated runtime (256 MB or more), which is great for safety but heavy for high-QPS scenarios. Think of Windmill as the artisan chef who makes each dish by hand — delicious, but not ideal when you need to serve 10,000 burgers in an hour.
🔗 n8n — The Connector
n8n is like Zapier on steroids, but self-hosted and much more flexible.
It excels at connecting external services: Slack, Stripe, Notion, databases, webhooks, timers — you name it. Its visual interface makes it easy to read, easy to share, and easy to debug.
But as workflows grow, visual sprawl sets in.
A dozen branches, nested loops, error handling — suddenly your “visual workflow” looks like a spaghetti diagram from a conspiracy movie. Long-running human-in-the-loop flows can also feel clunky. And yes, it now has AI Agent nodes, but if you compare them with a specialized AI orchestration platform like Langflow, they feel a bit… basic.
🧠 Langflow — The AI Orchestrator
If you do AI-driven workflows, Langflow feels like cheating.
It’s basically a visual LangChain: drag-and-drop LLMs, tools, vector stores, retrievers, memory components, and chain them in powerful ways. You can experiment with prompts, watch reasoning steps in real-time, and rapidly iterate.
But Langflow is laser-focused on AI use cases.
If you need to connect to SaaS APIs, schedule jobs, handle retries, or do database polling, you’ll likely wrap Langflow in something else (like n8n) to handle those responsibilities. It’s an AI brain, not a whole nervous system.
🏗 Temporal — The Heavyweight
Temporal is the “serious” one in the room. It’s code-driven, production-grade, distributed workflow orchestration.
It shines when you need strong consistency, retries, fault tolerance, long-running workflows, and high concurrency. Payments, order pipelines, state machines, human approvals — Temporal is designed to never lose state, even if your entire cluster crashes.
But be warned: Temporal is code-first.
There’s no pretty drag-and-drop interface. You’ll be writing workflow code, activity code, and deploying workers. It’s the most powerful of the bunch, but with great power comes a steeper learning curve.
2. Head-to-Head Comparison
Let’s put them side by side on the dimensions that actually matter.
3. When I Pick Each Tool
Here’s my decision matrix when I start a project:
- Windmill — internal glue logic, small automations, ad-hoc jobs, one-off scripts, quick web endpoints.
- n8n — external API integration, webhooks, cron jobs, notifications, “business glue”.
- Langflow — LLM agents, retrieval-augmented generation (RAG), multi-step AI reasoning.
- Temporal — critical backend processes that must be durable, auditable, recoverable, and scale under load.
In practice, I rarely use just one. Instead, I mix them:
Webhook → n8n flow (validate + route)
→ If AI decision needed → Langflow Agent
→ If long-running workflow → Temporal Workflow
→ For quick side tasks → Windmill script
4. The Reality Check
A few subtle but important clarifications:
Windmill is powerful, but don’t expect it to behave like a serverless clusterless Lambda farm. If you need to process 50k events/minute, you will need aggressive tuning and parallel workers — or just pick a different engine.
n8n’s visual workflows can stay maintainable if you use sub-workflows and templates — treat them like code modules.
Langflow is not a replacement for full workflow orchestration — it’s your AI layer. Pair it with n8n or Temporal for a complete solution.
Temporal is not free magic. You still need to think about idempotency, schema evolution, monitoring, and versioning. But it pays off massively for business-critical processes.
- Final Thoughts No single tool is “the best” — they’re like different instruments in an orchestra.
Windmill is the agile soloist for small, precise pieces.
n8n is the versatile connector, playing well with others.
Langflow is the AI virtuoso, brilliant at reasoning.
Temporal is the percussion section — steady, reliable, unflinching under pressure.
When you use them together, you can build a workflow system that’s robust, scalable, easy to iterate, and fun to work with.
TL;DR for Busy Engineers
Windmill → Best for low-frequency scripts + internal tools
n8n → Best for external integrations + readable flows
Langflow → Best for AI agents + RAG pipelines
Temporal → Best for high-concurrency, long-running, reliable workflows
Mix and match them like Lego bricks, and you’ve got yourself a solid workflow stack.
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