## Four criteria for agent and workflow tools Decide it on four criteria: - the apps the tool can act in - whether a human approves before it acts - whether you can self-host it and under what licence - what it costs as runs grow I score a tool only where its own vendor page supports the answer, and each criterion comes down to one question: - Apps it can act in: which business systems can it read from and write to? - Human approval: can a person stop an action before it happens? - Self-hosting and licence: can you run it on your own infrastructure, and under what terms? - Cost as runs grow: what does the bill look like at ten times today's volume? Where a page is silent, the entry reads "not stated on vendor page", not a guess. If an agent can't reach your CRM or ticketing system, it can only talk about them. n8n states 1000+ pre-built app integrations. It says workflows can start from webhooks, schedules or events in external systems. Whether an agent may send an email or change a record unattended comes down to human approval. Check the vendor page for an explicit approval step, not just a general claim about "control". Where customer data sits is settled by self-hosting. What you may do with the code is settled by the licence. Read both: a source-available licence and an open source one carry different limits on commercial use. On cost as runs grow, n8n's pricing page lists: - Starter at 20 – 24 € per month, so the billing choice alone moves that tier by 4 € - Pro runs 50 – 60 €, a 10 € gap - Business runs 667 € per month billed annually. Read the lower figure in each pair as the annual rate: the premium for staying flexible grows with the tier.  ## Langflow and n8n comparison table The n8n column holds source labels, and Langflow's price cell stays open because its pages state no per-execution plan; the subsection after the n8n criteria explains what you pay instead. The table below puts both tools through the same five rows, which means you can directly compare how each handles identical inputs. | | Langflow | n8n | |---|---|---| | Price and plans | No per-execution plan stated; see Langflow cost and licence | n8n.io/pricing, October 2026 | | Integrations | Components for model providers, vector stores and tools, per the [Langflow docs](https://docs.langflow.org) | Not stated on vendor page | | Approval | Not stated on vendor page | Not stated on vendor page | | Self-hosting and licence | Open source (MIT), self-hostable with pip or Docker, per its [repo](https://github.com/langflow-ai/langflow) | Not stated on vendor page | | Usage limits | Not stated on vendor page | n8n support, plan features, October 2026 | _Prices and plan limits checked against [n8n.io](https://n8n.io/vs/langflow) on October 17, 2026._ Langflow's approval and usage-limit cells are empty, so I can score neither from a vendor page. ### n8n against the four criteria Only pricing and limits are documented, and they are specific. On the pricing page: - The trial includes 1,000 executions a month - Starter (€20) includes 2,500 - Pro (€50) includes 10,000 - Business (€667) includes 40,000 That works out to roughly €0.008, €0.005 and €0.017 per included execution on the three paid plans. **Pro is the cheapest per run**, and Business costs more than three times as much per run as Pro, so you pay for the headroom rather than for a discount. Then there is concurrency, a separate ceiling. Per n8n's plan features page, the trial and Starter allow 5 concurrent executions, Pro-1 allows 20 and Pro-2 allows 50.  Pro-1 and Pro-2 are the two Pro sizes that page lists, with Pro-2 the larger. The page doesn't tie them to the prices above, so confirm which one you would be buying. A webhook-heavy workflow on Starter hits the limit when a sixth run starts while five are still going, whatever the monthly quota says. I leave apps, approval and self-hosting unscored, because the pages cited don't state them. The next step is to take the execution counts above and price your own monthly volume against them. ### Langflow cost and licence Langflow is open source under the MIT licence, published in its [GitHub repository](https://github.com/langflow-ai/langflow). That licence permits commercial use, so building a product on it carries no extra restriction of the kind a source-available licence adds. You can self-host it with no software fee, installing it with pip or running it from a Docker image. What you pay for is the server, storage, upgrades and whoever maintains it. A hosted version is also offered by the project's maintainers. I found no per-execution price for it on the pages I read, so confirm the hosted cost with the vendor before planning around it. The main cost is therefore infrastructure plus model-provider charges, not a per-execution plan. A flow that calls a model on every run is billed per token by the provider, and that line grows with usage whichever tool hosts the flow. n8n meters the workflow itself; Langflow meters nothing, and in both cases the model bill sits on top. ## Your situation and the right pairing Once you know your volume, the pairing is n8n for approval-driven business processes and Langflow for AI logic that other systems call. The exception is when you need both in a single execution trace. | Situation | Choice | Unless you need… | |---|---|---| | Business-app process with approvals | n8n | AI reasoning that you tune and test inside the same tool | | AI logic (RAG or agents) called from elsewhere | Langflow | Business steps such as routing, retries and approvals running in the same flow | | AI app feeding a workflow | Both, joined by a webhook or callback | One run history covering the AI step and the steps after it | ### Choose n8n if the job is a business process "New record arrives, a person approves, the workflow writes the result back" is the native shape of n8n, which is built around triggers, app nodes and branching. The condition to watch is AI depth. If the model call is one node among many, that works. If you spend most of your time iterating on retrieval or agent behavior, you are using a workflow tool for an AI-application problem. Choose Langflow if the job is the AI application. Langflow is where you assemble and test the retrieval pipeline or agent itself, then expose it for other systems to call. That suits a service whose output is an answer. The condition is everything after the answer. Approvals, retries and delivery into business apps live outside Langflow, so you'll need something else to handle them. ### Use both when AI logic feeds a workflow When AI logic feeds a workflow, the usual pattern is n8n calling a Langflow flow over a webhook, or Langflow calling back when it finishes. It works, but each product logs its own run. When a result looks wrong, you check one system, then the other, and match the two runs by hand. If that matching is a cost you can't absorb, the third row of the table is the one to reconsider. ## Where Activepieces fits in this decision ### A third option for agents that act across many apps For teams whose agent has to act across many business apps, with a person approving before it does, [Activepieces](https://www.activepieces.com/pricing), which MoneyGram, Moneypenny, Alan and FundingSocieties run, gives the agent a library of integrations to act in. It's not a verdict between Langflow and n8n. It's open source with an [MIT-licensed core](https://github.com/activepieces/activepieces/blob/main/LICENSE) and is self-hostable, and its AI agent step sits in the same flow as ordinary deterministic steps. The agent's reasoning and the actions around it therefore belong to one flow, not to two products joined by a webhook. ### Why one workflow means one log That matters because of the problem this article keeps returning to. When AI logic lives in one tool and business steps in another, a failed run means opening two logs and matching them by timestamp. In the Activepieces flow builder you can put an Agent step next to a plain step, such as "send this [Gmail](https://www.activepieces.com/pieces/gmail) message" or "append a row to [Google Sheets](https://www.activepieces.com/pieces/google-sheets)", both of which are among the 741 integrations counted in its open source monorepo, which means you can probably automate most of your team's common tools.  The run trace for that flow records a single execution from the first step to the last. There is no second system and no callback to reconcile, so there is one place to look when something goes wrong. ### Checking the connector library Where no connector exists, the agent can't act, so the connector library is the thing to check first. Activepieces calls its connectors "pieces", and the [pieces library](https://www.activepieces.com/pieces) holds **741 integrations in the open source monorepo**, roughly 60% of them contributed by the community rather than written in-house, so the platform's catalog grows with user demand rather than being limited by the core team.  When we looked, the page read "Showing 502 integrations", sorted by popularity, with Webhook, Google Sheets, Gmail and HTTP at the top. That's a count of what exists, not of what is maintained well. Look up the two or three apps your agent must write to and read how recently each integration was updated before trusting the headline number. If a connector is missing, the same library still covers you. The HTTP integration sends requests and returns responses, so an app without a dedicated integration can still be reached through its API, at the cost of building and maintaining that call yourself.  ### Approval steps, self-hosting and pricing to test Treat the rest as questions to test, not assumptions. Confirm how the approval step pauses a run, what the self-hosted version leaves out compared with the cloud one, and how pricing counts a run that contains an agent. If you want to test those against a real workflow, you can [try it free](https://cloud.activepieces.com/sign-up). Activepieces is the better fit for teams whose agent must act across many business apps with a person approving before it does, judged on whether the agent and its surrounding automation live in one place.  Because the Agent step sits beside ordinary deterministic steps in the same flow, the run trace shows a single execution from start to finish, rather than two systems bridged by a webhook or callback. Langflow and n8n each have their strengths, but if you want the agent's reasoning and its actions to be one flow and one log, Activepieces delivers that. ## A first-week test before committing to either tool Pick one real job from your own work, run it end to end on each tool's free or self-hosted option, and judge them on that rather than on a demo. 1. Write down the one job. Include the trigger, the apps it touches, the decision the model makes, and the action it ends in. "Triage inbound support emails and open a ticket for urgent ones" is testable. "Automate support" isn't. 2. Run it on both free or self-hosted options. Install Langflow's open-source version and n8n's self-hosted edition, or use their free cloud trials. Build the same job in each. Note where you had to leave one tool to finish the work, because every such exit becomes a webhook or callback you maintain. 3. Test the approval step. Make the agent propose an action, such as sending a refund or emailing a customer, and require a person to approve it first. Check whether the run pauses, who gets the request, and what happens if no one answers. Then see whether the approval, the model's reasoning and the final action appear in one run log or in two systems. 4. Estimate cost at your monthly runs. Take your expected runs per month and read each pricing page for what counts as a billable unit: an execution, a task, or a step. Add model-provider charges separately, since they sit outside either bill. A job with many steps can be cheap on one metric and costly on another. ## Frequently asked questions Yes, through HTTP: n8n can call a Langflow flow's API from a request step, and Langflow can trigger an n8n workflow by hitting a webhook. The catch is that **each product logs only its own half**. A failed support ticket then means reading two run histories and matching them by timestamp or an ID you pass along yourself. For a non-developer, n8n's canvas is built around connecting business apps, so an operations person usually starts with familiar concepts such as triggers, fields and conditions. Langflow assumes you are comfortable with models, prompts, vector stores and sometimes Python for custom components. You can self-host either tool without a software fee, since both publish self-hosting options. You still pay for the server, the database, upgrades and whoever is on call when it breaks. The licences also differ in wording, so read n8n's before building a product on top of it. ### Which is better for a customer support agent? Start with three narrower questions rather than product names. Can the agent read and update your helpdesk? Can a person approve a reply before it goes out? Can you see the model's reasoning and the follow-up business step in one trace? Langflow is …covers the model side, n8n covers the app-connection side, and the gap between them is where a support agent tends to fail. ### How often do these vendors change their pricing? Often enough that a figure copied from a blog post, including this one, should be treated as out of date. Plans, execution definitions and included usage get revised, and what counts as a billable run is the detail that moves your cost most. ## Related reading - [HubSpot Workflows: Types, Examples and Limits (2026 Guide)](https://www.activepieces.com/blog/hubspot-automation-workflows) - [How To Automate Competitor Price Change Alert](https://www.activepieces.com/blog/how-to-automate-competitor-price-change-alert) - [Langflow Alternatives: Top Tools to Try in 2026](https://www.activepieces.com/blog/langflow-alternatives-top-tools-to-try-in-2026) ## References - [n8n](https://n8n.io/pricing/) - [n8n](https://support.n8n.io/article/n-8-n-cloud-subscription-features-per-tier)**If the hard part is the AI behavior, build it in Langflow; if the hard part is the process around it, build it in n8n.