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n8n alternative: pick by what your runs have to leave behind

The right n8n alternative depends on what you're missing. Want a hosted builder? Zapier or Make. Another self-hostable engine? Activepieces, Windmill, Kestra, or Node-RED. A no-code AI agent platform? Lindy, Gumloop, or MindStudio. Runs that must leave a typed, owned record behind? Maskin.

Before you read further: Maskin is not a drag-and-drop no-code builder like Lindy or Pickaxe. It is code-optional, schema-first, and MCP-native. You do not wire nodes on a canvas. You configure loops (a trigger, the agents that work it, the typed objects it produces, the human gate, and the condition that closes it) against a schema that is the primitive. If a visual builder is what you came for, the list of four shapes below points you to the right tools, and most of this page is not for you.

The four shapes of n8n alternative

Every list that ranks for this search mixes four different kinds of tool. The four shapes are:

1. Hosted workflow builders (Zapier, Make)
2. Open-source and source-available workflow engines (Activepieces, Windmill, Kestra, Node-RED)
3. No-code AI agent platforms (Lindy, Gumloop, Pickaxe, MindStudio, Dust)
4. Closed-loop workspaces (Maskin)

Picking inside the wrong shape is the most common mistake. A team that wants to stop paying per task does not need a no-code AI agent platform. A team that wants an agent with an audit trail does not need a cheaper hosted builder.

Why people go looking for an n8n alternative

Four reasons come up, and they point to four different answers.

License terms. n8n's docs say its code is under the Sustainable Use License, a fair-code license, and that the license gives you free rights to use, modify, and redistribute with three limits: use only for your own internal business purposes or non-commercial use, distribute only free of charge for non-commercial purposes, and keep all licensing notices intact. Anything outside that needs a separate commercial agreement. The same docs call the product source-available and explain why n8n chose not to use an open source license (n8n community license docs). If you plan to embed n8n in a product you sell, read that page before you build. If you only run it for your own company, it rarely matters.

Hosting and metering. Self-hosting the Community edition is free, but you run the infrastructure. On paid plans, whether cloud or self-hosted, production executions count toward a quota. Per n8n's docs, a Schedule Trigger counts one execution every time it fires, and a Webhook Trigger counts every inbound request that activates it, including requests with an empty body (n8n: understand executions). A noisy webhook can use quota quickly.

The agent builder. n8n's AI Agent node lets you connect a chat model and one or more tools, and the agent decides which tool to call (n8n AI Agent node). n8n has also added standalone agents, currently in Preview: you configure a model, instructions, tools, skills, and a knowledge base, publish the agent, reach it through channels like Slack, run it on a schedule, and let it delegate to sub-agents. n8n stores each conversation as a session and offers session and episodic memory (n8n: build and manage agents). It can also pause for a person to approve or deny a specific tool call (n8n: human-in-the-loop for tools). If your reason for leaving is "n8n has no good agent story," that is getting less true.

The record. What n8n's docs describe storing is the run, the session, and the agent's memory. A business record that outlives any one agent (the account, the decision, the owner, the outcome) is something you model yourself, in a database or another store you wire in. That is the gap Maskin is built around, and it deserves its own section.

What n8n's docs say it stores about a run

The unit n8n stores is the execution, which its docs define as a single run of a workflow. Here is what the docs say about keeping them.

Source: n8n execution environment variables. The manage execution data page adds that annotated executions (for example, ones with tags or ratings) are never pruned, and that n8n recommends not saving unnecessary data so the database does not grow without limit. You can also attach your own fields to an execution with the Code node and filter by them later (customize executions data).

Read that as a design statement: the execution is a log of one run, kept for diagnostics, and tuned to stay small. That is the right design for a workflow engine. It also means the answer to "what did this agent decide about this customer in March, and did it work?" is something you build: a table or sheet, written by nodes you add, with a schema you invent and maintain.

Maskin starts from the other end. In a loop, a signal enters as an insight (from Slack, Intercom, PostHog, GitHub, Gmail, or a scheduled scan), gets shaped into a bet with a win condition, runs as tasks that humans and agents share, and closes with the outcome measured against that condition. Insights, bets, and tasks are objects in one schema, linked by typed relationships, and the UI and the agents read the same objects over the same API. The objects are the work, not a log of it, so there is no log-retention setting to tune.

A worked example: churn-risk alerts

Take a loop that watches product usage and flags accounts at risk of churning.

In n8n you would build a Schedule Trigger, query your analytics, branch on a threshold, call an AI Agent node to draft the alert, and post to Slack. That works well. To answer a month later whether the flagged accounts actually churned, you would add nodes that write each alert to a table, add another run that updates it when the account renews or cancels, and build the view yourself. The execution log tells you the run happened. It was not designed to be the table.

In Maskin the same loop creates an insight per at-risk account, linked to the source signal. It is promoted to a bet ("save this account by Friday") with an owner. Tasks hang off the bet. When the account renews or leaves, the bet closes with an outcome, and anyone who reads the graph, person or agent, can see how similar bets ended. The record is the output of the loop, not a side project beside it. The full pattern is written up as how Maskin turns an at-risk signal into a recovery bet.

The alternatives, grouped honestly

We have not benchmarked these tools for this page. What follows describes shape, not performance; check each vendor's documentation for current features, limits, and pricing.

Hosted workflow builders

Zapier and Make are hosted, visual builders with very large integration catalogs. Choose them if you want zero infrastructure and a non-technical team that lives in the builder. You trade away self-hosting and, usually, fine control over the runtime.

Open-source and source-available workflow engines

These are the closest like-for-like alternatives if your problem is the license or the hosting model.

• Activepieces is a self-hostable, open-source visual automation builder in the same general shape as n8n. GitHub does not detect a single standard license for its repository, so read the license file before you commit.
• Node-RED is a flow-based programming tool with an Apache 2.0 license per its GitHub repository. It leans toward developers and event wiring more than business users.
• Windmill turns scripts into workflows and internal tools. It is code-first, so it suits engineering teams more than operations teams. GitHub does not detect a single standard license for its repository either, so read the license file.
• Kestra is a declarative orchestration engine you configure in YAML, with an Apache 2.0 license per its repository. It fits data and platform teams.
• Huginn is an older, MIT-licensed project where agents watch the web and act on events. It is simple and self-hosted.

No-code AI agent platforms

If the search that brought you here was really "no code AI agent builder," this is your shape: Lindy, Gumloop, Pickaxe, MindStudio, Dust, and Glean's agent builder all let a non-programmer configure an agent's prompts, tools, and triggers visually. They are the names that recur in the listicles that rank for "no code ai agent." They are mostly hosted products and generally optimize for the first working agent in an afternoon.

Open-source LLM app builders such as Flowise, Langflow (MIT per its repository), and Dify sit between this shape and the engines above: visual, self-hostable, and aimed at building LLM apps and agents. GitHub detects no single standard license for the Flowise and Dify repositories, so read their license files.

Code-first durable engines

Temporal and similar durable-execution engines run long-lived processes as code, with state that survives restarts, including one process per business entity that runs for months. They are powerful and entirely code-first, so they suit engineering teams and not the non-engineers who often search for an n8n alternative.

Closed-loop workspaces

Maskin is the entry here. It is the one on this page where the output of a run is a typed object a person owns, rather than a log, a canvas, or code.

How the shapes compare

Where the closed-loop shape wins

Three situations tip the decision.

The record outlives the agent. If the question after every run is "who is responsible for this, and did it work?", an execution log or an agent's session memory answers part of it: what ran, and what the agent remembers. A bet with a driver and a win condition answers the rest, and it stays put when you swap the agent or the model. See agentic workflow vs the closed loop for the longer argument.

The work spans weeks. A deal, an account, a release. A run is a moment; the thing you care about lives for weeks. Maskin's own outbound loop carries hundreds of contacts through stages, and most of its steps run on schedules rather than events, because those contacts stay in motion for weeks.

People sit at defined points on the record. n8n can pause on a specific tool call for approval. In a loop the human gate attaches to the object instead: you decide where a person promotes, approves, or closes, and the loop is bounded by close conditions rather than running open-ended. See human in the loop AI.

If none of the three describes your problem, you probably want one of the other shapes.

When n8n is the right answer

Stay on n8n, or pick another engine from the groups above, when:

• You need breadth of connectors. n8n's integration catalog is much larger than Maskin's provider list. If your work is moving data between many different apps, a builder with a big catalog is the better tool.
• You want a visual canvas your team can read. Maskin has no node canvas. Loops are configured against a schema. If a teammate who does not write code needs to see and edit the flow as a diagram, n8n, Make, or a no-code AI agent platform serves them better.
• The job is plumbing. Sync a form to a CRM, post a digest to Slack, resize images, call an API on a timer. That is what an engine is for, and a typed object graph around it is overhead. (A Danish-language rule of thumb for when fixed rules beat an agent: AI-automatisering: de første opgaver at automatisere.)
• A log is enough. If "the run succeeded or failed, and I can look at it for two weeks" answers your questions, you do not need a persistent record. Tune the pruning settings in the table above and move on.
• You want to run it yourself with little setup. n8n's Community edition is free and mature. Maskin's self-hosted version currently requires Docker on the host.

And a plain restatement of the wedge: if what you want is a drag-and-drop no-code builder, Maskin is the wrong tab. Maskin is for teams who would rather define a schema once and have every agent and every person work against it.

Using n8n and Maskin together

These are not mutually exclusive. Maskin exposes its objects over MCP, and n8n documents connecting MCP servers as tools for its AI Agent node (n8n AI Agent node). So a design where n8n keeps doing the plumbing and writes outcomes into Maskin objects is technically plausible. We have not published a tested recipe for it, so treat it as an option to prototype, not a documented integration. The tool surface is in the MCP tools reference.

Maskin in one pass

Maskin is open source under Apache 2.0, MCP-native, and built for team knowledge work. Agents run in isolated, sandboxed container sessions. You bring the model; the workspace is model-agnostic at the MCP layer. Self-hosting is free, with no open-core gate on the software. The longer versions of this picture live in the closed-loop workspace and the three tiers of AI-native work. Maskin is early, so evaluate the architecture rather than a long vendor track record. If you are comparing against other open-source options for different jobs, the alternatives hub covers Jira, Notion, Codex, and AI SDR tools, and the open source alternative to Codex page is the closest sibling to this one. If the agent work you manage is an org chart of AI employees rather than a workflow, the Paperclip alternative page applies the same method to that shape.

Getting started

• Self-host free forever. Clone the repo (Apache 2.0), follow the onboarding, and run agents in sandboxed container sessions on your own infrastructure. The current implementation requires Docker on the host.
• Start a hosted trial. Managed plans with EU and US data residency start at Pro $20/seat/month (agent credits included); Team is $200/workspace/month with unlimited seats; Enterprise runs on your own LLM and infrastructure.

FAQ

What is the best n8n alternative?

It depends on what you are missing. Zapier or Make if you want hosted and visual. Activepieces, Node-RED, Kestra, or Windmill if you want a self-hostable engine. Lindy, Gumloop, or MindStudio if you want a no-code AI agent builder. Maskin if every run must leave a typed record with an owner. Most "best of" lists mix these shapes, which is why they feel unhelpful.

Is n8n open source?

n8n's own docs describe it as fair-code and source-available under the Sustainable Use License, not an OSI-approved open source license. You can use and modify it for your own internal business purposes or non-commercial use; other uses need a commercial agreement. Fully open-source alternatives include Node-RED and Kestra (Apache 2.0) and Huginn (MIT), per their repositories.

What is the best free alternative to n8n?

For a free, self-hosted engine, start with Activepieces, Node-RED, or Windmill and read each license. For a free workspace where agents produce typed records, Maskin's self-hosted version is free under Apache 2.0, and you pay only for your own infrastructure and model usage.

Is there a no-code AI agent alternative to n8n?

Yes. Lindy, Gumloop, Pickaxe, MindStudio, and Dust let non-programmers configure agents visually. Maskin is not in this group: it is code-optional and schema-first, with no drag-and-drop canvas. Choose a no-code AI agent platform for the fastest first agent. Choose Maskin when agents need owners, approval points, and measured outcomes.

Can Maskin replace n8n?

Not as a drop-in. n8n is a workflow engine with a large integration catalog, a visual canvas, and its own agents; Maskin is a workspace where loops produce typed objects. If your work is app-to-app plumbing, keep n8n. If your pain is the missing business record and ownership around agent runs, Maskin addresses that layer, and you may run both.

Does n8n keep a history of runs?

Yes, with limits. By default n8n saves execution data on success and on error, and prunes finished executions after 336 hours (14 days) or when more than 10,000 are stored. Annotated executions are not pruned. You can change these settings with environment variables, or add your own fields to executions and filter by them.