Best Free & Open Source Alternatives to Make

n8n (short for "nodemation") is an open-source workflow automation tool that lets you connect apps, APIs, and services to automate tasks. It features a visual, drag-and-drop interface and supports over 400 integrations out of the box.

You can create custom workflows that handle data transfers, trigger actions, and process logic using built-in nodes or your own JavaScript code. n8n can be self-hosted for full control over data and privacy, or used via their cloud offering.

Whether you're a developer or a non-technical user, n8n helps automate repetitive tasks and build complex workflows without writing a lot of code.

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring data workflows. Pipelines are defined as Python code, giving teams the full flexibility of a programming language to create dynamic, parametrised workflows without the constraints of XML or graphical configuration tools. A built-in web interface provides real-time visibility into task status, logs, and execution history.

Key capabilities include a rich library of operators for integrating with major cloud providers (AWS, GCP, Azure) and third-party services, a modular message-queue architecture that allows worker capacity to scale horizontally, Jinja templating for dynamic pipeline parameters, and support for custom operators when the built-in integrations do not cover a use case.

Airflow is designed for data engineers, machine learning teams, and platform engineers who need a reliable, transparent, and highly extensible system for orchestrating complex multi-step pipelines — from ETL jobs and ML model training to infrastructure provisioning and scheduled reporting.

Project status: Flowise has been discontinued. Active development stopped in July 2026, the GitHub repository was archived on August 13, 2026, and official support ended on August 31, 2026. The Apache 2.0 source remains available to self-host or fork.

Flowise is an open source, low-code platform for building AI agents and LLM-powered applications through a visual, drag-and-drop interface — think of it as "AI LEGO." Instead of starting from code, you assemble chatbots, RAG pipelines, and multi-agent workflows by connecting nodes on a canvas, then deploy them as APIs or embedded chat widgets. With more than 55,000 GitHub stars, it became one of the most popular projects in the AI agent builder space.

Flowise offered three builders for different levels of complexity:

• Chatflow — flexible chatbots and single-agent workflows using advanced RAG, retrievers, and rerankers
• Agentflow — multi-agent orchestration with branching, loops, parallelization, and hierarchical logic
• Assistant — beginner-friendly agents that follow instructions, use tools, and answer questions from uploaded files

Core features include:

• 100+ integrations with LLM providers, vector stores, tools, memory, and external services
• A visual canvas with branching, looping, custom code, and logic routing
• Observability through execution logs, visual debugging, and external log streaming
• API access to every flow, plus JavaScript and Python SDKs, a CLI, and auto-generated Swagger docs
• Embedded chat widget, template marketplace, and reusable components
• Enterprise features such as SSO, RBAC, encrypted credentials, rate limiting, and scaling for high-throughput workloads

Under the hood, Flowise is a TypeScript monorepo: a Node.js backend that executes flows and serves them over REST, a React frontend for the drag-and-drop editor, and a components package containing the third-party integration nodes.

Self-hosting options:

• npm — install with npm install -g flowise and run npx flowise start (requires Node.js ≥ 20), then open http://localhost:3000
• Docker — a standalone image and a docker-compose setup ship with the repository
• One-click templates — AWS, Azure, GCP, DigitalOcean, Railway, Render, Hugging Face Spaces, and others
• A hosted Flowise Cloud offering was also available

Flowise launched in 2023, was backed by Y Combinator, and grew a user base spanning indie makers to enterprises. After end of life, the core team ships no further updates or security patches — if you rely on Flowise today, plan to run your existing self-hosted instance as-is, maintain a community fork, or migrate to another open source agent builder.

Dify is an open source LLM application development platform that takes teams from prototype to production without rebuilding their stack. Where Flowise-langflow-style canvas tools focus on single flows, Dify is a fuller product: it wraps visual workflows, a RAG pipeline, agent capabilities, prompt tooling, and LLM operations into one collaborative workspace with role-based access for teams.

Key features:

• Visual workflow canvas for building and testing AI workflows on a node-based canvas
• Comprehensive model support — hundreds of proprietary and open source LLMs from dozens of providers, including any OpenAI-API-compatible models
• Prompt IDE for crafting prompts, comparing model performance, and adding features like text-to-speech to chat apps
• RAG pipeline covering document ingestion to retrieval, with built-in text extraction from PDFs, PPTs and other formats
• Autonomous agents with a code-execution sandbox that can run commands, install software, and handle files; extendable with tools from the Dify Marketplace, MCP servers, or your own APIs
• LLMOps for monitoring app logs and performance over time, and iterating on prompts, datasets, and models from production data; integrates with Langfuse, Opik and Arize Phoenix
• Backend-as-a-Service — every capability ships with corresponding APIs so Dify can sit behind your own product

Self-hosting options:

• Docker Compose — clone the repo, cd docker && cp .env.example .env && docker compose up -d, then open http://localhost/install (needs 2+ CPU cores and 4+ GiB RAM)
• Source code deployment guide for contributors and custom builds
• Community, Enterprise, and Cloud editions — the managed cloud includes a free sandbox plan; enterprise adds SSO and RBAC

Dify is built with a TypeScript/Next.js frontend and a Python backend. Its Community Edition is free to self-host under the Dify Open Source License (Apache 2.0 with additional conditions restricting multi-tenant hosting and frontend branding removal) — for internal business use it behaves like the free open source alternative to building LLM apps top-to-bottom on proprietary platform APIs.

Langflow is an open source, Python-based visual builder for AI agents and LLM applications. Instead of writing orchestration glue code, you design multi-agent workflows and RAG pipelines by arranging components on a drag-and-drop canvas — then deploy with one click, where every flow automatically becomes a REST API endpoint callable from any framework or stack. The project is stewarded by DataStax (the Astra DB company) and offers a free, enterprise-grade managed cloud alongside self-hosting.

Key features:

• Visual builder with an interactive playground for testing and refining flows step by step
• Full source code access — any component can be customized or replaced with Python
• Support for all major LLM providers, vector databases and a growing library of AI tools
• Multi-agent orchestration with conversation management and retrieval
• Deploy flows as a REST API or as an MCP server, so external clients can call your flows as tools
• Observability integrations with LangSmith, Langfuse and others
• Export flows as JSON or Python code to embed in your own applications

Self-hosting options:

• pip / uv — uv pip install langflow then uv run langflow run (requires Python 3.10–3.14); starts at http://127.0.0.1:7860
• Docker — docker run -p 7860:7860 langflowai/langflow:latest
• Langflow Desktop — a native Windows/macOS app with all dependencies bundled, no Python environment needed
• Deployment guides exist for all major clouds, or use DataStax's free managed cloud

Compared with hosted agent-builder SaaS, Langflow trades a locked-down web console for openness: it is MIT-licensed, model-, API- and database-agnostic, and stays extensible with custom Python components. It's a natural landing spot for teams coming from Flowise, which was discontinued in 2026 — the drag-and-drop model is similar, while Langflow remains under active development.

Sim is an open-source workspace for building, deploying, and orchestrating AI agents and agentic workflows. It combines a visual workflow builder, a conversational chat interface, and code-based agent definitions into a single platform, letting teams choose how they build — visually, conversationally, or programmatically.

Beyond agent building, Sim brings together everything an AI workforce needs in one workspace: built-in tables for structured data your agents can query, a shared file store, knowledge bases with synced documentation, and scheduled tasks that run agents on a recurring calendar. It connects to 1,000+ integrations including Slack, Notion, HubSpot, Salesforce, and databases, and supports every major LLM from OpenAI, Anthropic, Google, and others — plus local models via Ollama and vLLM.

Sim is fully self-hostable via Docker Compose or npx simstudio and is licensed under Apache 2.0. A managed cloud is available at sim.ai for teams that prefer a hosted option. Compared to proprietary AI agent platforms like Lindy AI, Relevance AI, and Voiceflow, Sim offers the same core capabilities — agent building, workflow orchestration, knowledge management, and team collaboration — with full source-code access and no vendor lock-in.

Kestra is an open-source orchestration and scheduling platform for building data pipelines, DevOps workflows, and business automation at scale. Workflows are defined in YAML (or via the built-in visual editor), and Kestra provides a rich plugin ecosystem with over 500 integrations covering databases, cloud providers, messaging systems, APIs, and scripting languages.

The platform supports both event-driven triggers and cron-based scheduling, with features like parallel task execution, dynamic inputs, error handling, retries, and sub-workflows. A real-time execution dashboard and detailed logs make it easy to monitor and debug complex pipelines. Kestra is designed for high availability and can scale horizontally for enterprise workloads.

Compared to Zapier or Make, Kestra is a developer and data-engineering tool: pipelines are code-first, version-controlled, and built for reliability rather than ease of use by non-technical users. It is self-hostable via Docker or Kubernetes and offers an enterprise cloud tier for teams that need managed infrastructure.

Trigger.dev is an open-source workflow orchestration platform built for developers who want to define automation in code, not in visual GUI editors. You write TypeScript tasks — background jobs, scheduled crons, event-driven pipelines, or long-running AI agents — and Trigger.dev handles the execution infrastructure: retries, timeouts, concurrency limits, real-time logging, and a dashboard for monitoring runs.

It integrates natively with Next.js, Remix, and other Node.js frameworks via an SDK, so you can trigger workflows directly from your API routes or webhooks. The platform also supports durable execution — if a task is interrupted mid-run, it resumes from where it left off rather than restarting from scratch. This makes it particularly well-suited for AI agent pipelines that call slow LLM APIs or perform multi-step operations.

Compared to Zapier or Make, Trigger.dev is squarely developer-first: workflows live in your codebase, are version-controlled, and are tested like any other code. It is self-hostable via Docker and also available as a managed cloud service.

Rowboat is a local-first desktop AI coworker that indexes your work into a living Obsidian-style backlinked knowledge graph. It includes built-in work surfaces for collaborating with AI: an email client that auto-drafts responses using work context, a built-in browser isolated from your main browser, a meeting note-taker with live transcription, and a code mode for parallel coding agents.

Unlike most AI tools that reconstruct context on demand, Rowboat maintains long-lived knowledge that compounds over time. Context accumulates, relationships are explicit and inspectable, notes are editable by you, and everything lives on your machine as plain Markdown. It supports bring-your-own-model (Ollama, LM Studio, or any API), MCP tool integrations, and background agents that run on events or schedules.

Automatisch is a workflow automation platform that lets teams automate business processes without writing code, functioning as a self-hostable alternative to Zapier. Through a visual interface, users connect popular services — including GitHub, Discord, Firebase, PostgreSQL, Twilio, Typeform, and more — and define trigger-based workflows that run automatically.

Because Automatisch runs on your own infrastructure, all data stays within your environment. This makes it a strong fit for organisations in healthcare, finance, or other regulated industries that cannot rely on third-party cloud automation services. GDPR compliance is built in by design.

Automatisch is aimed at businesses and technical teams that want the productivity benefits of automation without vendor lock-in or the data-sovereignty concerns that come with hosted SaaS platforms.

Tale is an open-source workspace where human teams and AI agents work on projects together. Work is organized as tasks on a project board: you write the brief with source files and completion criteria, assign teammates or configured project agents, then follow progress, steer agents with @mentions, and review their reports and delivered files. A manager agent can keep the queue moving by delegating ready tasks, and repeatable processes can be captured as versioned automations with scheduled starts, workflow steps, and connector approvals.

Agents run in persistent sandbox workspaces with configurable concurrency, and each project agent can be set up with its own instructions, harness, model, and tools. Tale supports popular coding-agent runtimes including Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and others, and works with your own provider API keys or supported vendor subscriptions. Built-in chat with side-by-side model comparison, shared project knowledge with retrieval, and governance guardrails round out the platform.

Tale is MIT-licensed and self-hostable with Docker Compose or the Tale CLI, with a managed cloud service and an Enterprise tier (same features plus professional operation and support) for teams that prefer not to run it themselves. Compared with proprietary project tools like Asana, Linear, or ClickUp, Tale adds the AI-agent execution layer natively; compared with agent platforms like Lindy AI or Relevance AI, it keeps the full project context — tasks, files, knowledge, and reviews — in one self-hostable workspace.

Automa is a browser extension for Chrome and Firefox that lets you automate repetitive browser tasks by building visual workflows. You connect blocks — click, fill form, extract data, loop, condition, wait — in a drag-and-drop canvas, and Automa executes them in sequence. No code required, though JavaScript blocks are available for power users.

Use cases range from auto-filling forms and scraping data from websites, to scheduling recurring tasks and chaining multi-step workflows. Workflows can be triggered manually, on a schedule, or via keyboard shortcut. Extracted data can be exported to CSV, Google Sheets, or sent to a webhook.

Compared to Zapier or Make, Automa focuses specifically on browser-based automation — it lives in the browser, not on a server, so it can interact with any website you can open, including those without public APIs. For personal productivity and light web scraping without spinning up a headless browser service, Automa covers a lot of ground with minimal setup.