The open-source alternatives to Make are n8n, Activepieces, Dify, Langflow, Windmill and Huginn. n8n and Activepieces give the closest thing to Make's visual canvas, with n8n's larger integration library and Activepieces's MIT licence. Dify and Langflow build AI workflows and agents visually, with Langflow turning each flow into an API or MCP server. Windmill is code-first for developers, and Huginn runs rule-based automations. Check the licences: n8n and Dify are source available.
4 of 6 carry an OSI licence. 6 of 6 run on your own machines. Figures read from GitHub on September 17, 2026.
One project per need, with the reason from its own README and the one thing to know before you choose it.
1,500+ integrations and 9,000+ templates by its own count, with AI agents, JavaScript and Python steps, and role-based access, self-hosted or in its cloud.
Know firstSource available: its licence allows internal, personal and non-commercial use only, and enterprise files need a paid licence.
MIT Community Edition whose TypeScript pieces double as MCP servers, with loops, branches, approvals, code steps and an AI SDK.
Know firstEnterprise features sit in paid folders, and its README gives two different integration counts.
MIT visual builder for agents and workflows, with Python-editable components and every flow deployable as an API or MCP server.
Know firstBuilt around AI components rather than app-to-app connectors, with no integration count given.
Visual AI workflows, retrieval pipelines, agents with their own sandbox, MCP tools and hundreds of models, with an API for everything built.
Know firstSource available: no multi-tenant use and no logo removal without written permission.
Scripts in Python, TypeScript, Go, Bash, SQL and more, composed into flows with generated UIs, triggered by schedules, webhooks, Kafka or email.
Know firstAGPLv3 from source, but the ready-made images carry extra terms and may not be resold.
MIT since 2013, with agents that watch the web, send and receive webhooks, run JavaScript and email you when a page changes.
Know firstNo AI features at all, and fewer integrations than the modern platforms.
The facts that decide most choices. Each project's page carries the full panel.
| Project | Best for | Openness | Licence | Runs as | Own machines | Stars |
|---|---|---|---|---|---|---|
| n8n | The largest integration library | Source available | See LICENSE | Docker, Install script, Hosted cloud | Yes | 205,074 |
| Activepieces | A no-code builder under MIT | OSI | See LICENSE | Self-hosted (see its deploy guide), Hosted cloud | Yes | 24,531 |
| Langflow | Visual AI flows as APIs or MCP tools | OSI | MIT | pip (uv), Docker, Desktop app | Yes | 154,959 |
| Dify | AI-first workflows and agents | Source available | See LICENSE | Docker Compose, Hosted cloud | Yes | 156,209 |
| Windmill | Code-first automation for developers | OSI | See LICENSE | Docker Compose, Kubernetes (Helm) | Yes | 17,958 |
| Huginn | Private rule-based automations | OSI | MIT | Docker, Ruby install, Heroku | Yes | 49,969 |
Licence and star figures read from GitHub on September 17, 2026. Stars are shown as a dated fact and were not used to rank this page.
A fair-code workflow automation platform with 1,500+ integrations, 9,000+ templates, AI agents over OpenAI, Anthropic, Google or open models, and code steps in JavaScript and Python, under the Sustainable Use License.
1,500+ integrations and 9,000+ templates by its own count, with AI agents, JavaScript and Python steps, and role-based access, self-hosted or in its cloud.
Know firstSource available: its licence allows internal, personal and non-commercial use only, and enterprise files need a paid licence.
An MIT automation builder its README calls an open-source replacement for Zapier, with hundreds of TypeScript pieces that double as MCP servers, AI agents, human approval steps and a no-code builder.
MIT Community Edition whose TypeScript pieces double as MCP servers, with loops, branches, approvals, code steps and an AI SDK.
Know firstEnterprise features sit in paid folders, and its README gives two different integration counts.
An MIT visual platform for building and deploying AI agents and workflows that turns every flow into an API or an MCP server, with Python-editable components and multi-agent orchestration.
MIT visual builder for agents and workflows, with Python-editable components and every flow deployable as an API or MCP server.
Know firstBuilt around AI components rather than app-to-app connectors, with no integration count given.
An LLM app development platform with visual AI workflows, RAG pipelines, sandboxed agents, MCP tools and hundreds of models, under Apache-2.0 plus conditions that forbid multi-tenant use and removing the logo without permission.
Visual AI workflows, retrieval pipelines, agents with their own sandbox, MCP tools and hundreds of models, with an API for everything built.
Know firstSource available: no multi-tenant use and no logo removal without written permission.
A developer platform for scripts, workflows, background jobs and internal UIs in Python, TypeScript, Go, Bash, SQL and more, AGPLv3 when built from source, with triggers from schedules, webhooks, Kafka and email.
Scripts in Python, TypeScript, Go, Bash, SQL and more, composed into flows with generated UIs, triggered by schedules, webhooks, Kafka or email.
Know firstAGPLv3 from source, but the ready-made images carry extra terms and may not be resold.
An MIT system, running since 2013, for building agents that watch the web and act for you, which its README calls a hackable version of IFTTT or Zapier on your own server, with webhooks, scraping and JavaScript steps.
MIT since 2013, with agents that watch the web, send and receive webhooks, run JavaScript and email you when a page changes.
Know firstNo AI features at all, and fewer integrations than the modern platforms.
The questions that settle it, in the order they usually come up.
n8n, Activepieces and Dify give a canvas; Windmill and Huginn expect code or configuration.
Dify for agents with a sandbox, Langflow for flows exposed as APIs or MCP servers.
Activepieces, Langflow and Huginn allow it under MIT; n8n, Dify and Windmill's images need an agreement.
Make, in the vendor's own words, is "the visual AI automation platform", by its own description on its homepage. make.com. The cases below are where that still wins.
Stars, licence, last commit and last release are read from the GitHub API by a script every week and carry the date they were read. Nobody types them.
What each project does is written from its own README and LICENSE, read in full on the date shown, and phrased as the project's claim. The sources are listed at the end.
That a project is faster, better or cheaper than Make. A price for anyone. A user count. A roadmap. If a fact is not on this page, it was not in the source.
Activepieces (MIT) or n8n for a visual builder closest to Make's canvas. Langflow (MIT) or Dify when the workflow is AI-first, with Langflow deploying flows as APIs or MCP servers. Windmill for developers who prefer scripts, and Huginn for private rule-based automations.
n8n combines a visual canvas with code steps, Activepieces has a no-code builder with loops, branches and approvals, Dify builds AI workflows on a visual canvas, and Langflow is a visual authoring tool for agents. Windmill and Huginn are code and configuration first.
Activepieces, Langflow and Huginn are MIT. Windmill built from source is AGPLv3, with extra terms on its ready-made images. n8n's Sustainable Use License allows internal and non-commercial use only, and Dify forbids multi-tenant use and logo removal, so this directory labels those two source available.
Partly. n8n claims 1,500 integrations and Activepieces counts its pieces at 200 to 280, against the thousands Make and Zapier advertise. For a common tool the connector usually exists; for a rare one you may write it, which both platforms support in TypeScript or code steps.
Dify, if the agent needs its own sandbox to run commands and handle files, or Langflow, if you want to build an agent visually and expose it as an MCP server for other tools. n8n and Activepieces also build agents alongside ordinary automations.
Running one of these inside your own environment, with your own data and your own security rules, is the kind of work Reveneau does. Read how a forward deployed engagement works.