Originally published athttps://www.nocobase.com/en/blog/14-ai-low-code-platforms-github
Recently, while browsing the r/AI_Agents subreddit on Reddit, I came across a question that felt surprisingly real:
“Is there any low-code tool that actually lets AI execute tasks and run workflows?”
It sounds like a simple question, but it hits a pain point many developers share. There are plenty of “AI-powered low-code platforms” out there, but most of them only add a chat box — maybe they generate some SQL or form fields. But tools that let AI truly run workflows, call APIs, and function as an agent are still rare.
Then the comments started to split. Someone bluntly said:
“These AI no-code platforms won’t last a year. If AI is really that powerful, it shouldn’t still rely on drag-and-drop flowcharts to work.”
It’s a harsh take, but it also reflects a real concern: If AI is already so capable, why do we still need no-code? Are these drag-and-drop tools destined to become obsolete?
The interesting thing is — that comment was posted eight months ago. And today, not only are these tools still around, many have grown more mature, with even more new projects emerging.
Just a few days ago, we compiled 💡The Most Popular Open-Source No-Code AI Tools on GitHub. Looking at stars, community activity, and feature maturity, many of these tools are not only alive — they’re actively improving and expanding their AI capabilities.
This shows that no-code/low-code + AI isn’t a short-lived hype — it’s something real, being built, used, and evolving.
That said, skepticism still matters. Another comment put it clearly:
“Look into open-source alternatives — most low-code AI tools are still in their early stages.
But most importantly: know exactly what problem you’re solving before choosing a tool.
A lot of so-called ‘AI Agents’ are really just traditional automation with an LLM stuck on top.”
It’s hard to argue with that. Many “AI agent platforms” do little more than connect automation flows to an LLM — smarter on the surface, but still just tools.
We’ve covered related topics before, like 💡Noteworthy Open-Source AI Agent Projects and 💡Great Open-Source AI Tools.
But this time, instead of asking “Can AI build systems?”, we’re shifting the focus naturally from no-code to low-code.
No-code is about enabling non-technical users to use AI — you don’t write code, but you can call models or generate content.
Low-code, however, is for people who understand the business and know a bit of tech — it focuses on data modeling, process logic, permission systems, and plugin extensibility, which are closer to real system building.
So, we looked into the GitHub“low-code” topic and focused on tools that:
- ⭐ Have active communities and good maintenance (stars, updates, feedback)
- 🤖 Clearly provide AI capabilities in their docs, not just a chatbot window
- 🛠️ Support self-hosting or extensions and can actually be used in production environments
Based on this, we shortlisted 14 representative low-code platforms that truly integrate AI.
They each take different paths — some help build business systems, some focus on agent workflows, and some specialize in data and spreadsheet applications.
Before diving into details, here’s an overview table showing how each of these 14 tools positions AI 👇
Comparison Table (AI Features × Tool Positioning Overview)
| Tool | Category | How AI is Used | Workflow Extensible | Supports Modeling/UI | Table/Data Support | Cross-domain Capability |
|---|---|---|---|---|---|---|
| NocoBase | Business Apps | AI generates data models, pages, logic | ✅ Plugins + Automation | ✅ Full | ✅ | ⭐ Strong (Data + System + Flow) |
| ToolJet | Business Apps | AI Copilot generates pages/API scripts | ⚠️ Light Flows | ✅ | ⚠️ | Medium |
| Appsmith | Business Apps | AI generates SQL and form logic | ⚠️ | ✅ | ⚠️ | Medium |
| Budibase | Business Apps | AI fills fields, generates form text | ⚠️ | ✅ | ⚠️ | Weak |
| refine | Business Apps | AI generates CRUD logic and code | ❌ | ✅ (code-focused) | ❌ | No |
| n8n | Workflow/Agent | AI tasks, API calls | ✅ | ❌ | ❌ | No |
| Dify | Workflow/Agent | Agents, knowledge base, tool calling | ✅ | ❌ | ⚠️ | No |
| Flowise | Workflow/Agent | RAG, visual LLM chains | ⚠️ | ❌ | ⚠️ | No |
| Kestra | Workflow/Agent | AI generates YAML workflows | ✅ | ❌ | ❌ | No |
| Node-RED | Workflow/IoT | AI nodes + device/event automation | ⚠️ | ❌ | ❌ | No |
| Sim | Workflow/Agent | Multi-agent collaborative workflows | ⚠️ | ❌ | ❌ | No |
| NocoDB | Data Tables | AI fill, smart fields, insights | ⚠️ | ❌ | ✅ | Weak |
| Teable | Data Tables | AI chat and auto-generated reports | ⚠️ | ❌ | ✅ | Weak |
| Onlook | AI UI | AI generates UI/components/React code | ❌ | ⚠️ (UI-focused) | ❌ | Special (UI-focused) |
AI-Powered Business Application Builders
These tools don’t just use AI to answer questions or trigger workflows — AI is directly involved in building the application itself.
They help users quickly create data models, forms, pages, permissions, and internal business systems, making them ideal for CRM, approvals, ERP, admin dashboards, and data-entry platforms.
NocoBase
⭐ 17k
Website: https://www.nocobase.com/
GitHub: https://github.com/nocobase/nocobase
- Overview: An open-source low-code platform featuring real AI agents that can assist in both building and using applications. AI can help create data models and page layouts during development, and later handle queries, analyze data, or answer business questions.
- Target users: Teams building business systems — internal IT teams, B2B product teams, system integrators.
- Use cases: CRM, approval workflows, project management, order and asset systems, with plugin support for automation and AI-driven modeling.
ToolJet
⭐ 36.8k
Website: https://www.tooljet.ai/
GitHub: https://github.com/ToolJet/ToolJet
- Overview: A low-code platform for internal enterprise apps, combining AI Copilot + visual UI building + API integration.
- Target users: Engineering, operations, or data teams that need internal dashboards fast.
- Use cases: Admin panels, internal tools, API-based applications where AI can help write SQL, generate layouts, or scripts.
Appsmith
⭐ 38.3k
Website: https://www.appsmith.com/
GitHub: https://github.com/appsmithorg/appsmith
- Overview: Open-source internal app builder with Appsmith AI for automatic SQL generation, form logic, and component setup.
- Target users: Front-end developers, data tool builders, teams creating CRUD systems.
- Use cases: Query dashboards, internal admin tools, database utilities — e.g. natural language → AI generates SQL → table output.
Budibase
⭐ 27.2k
Website: https://budibase.com/
GitHub: https://github.com/Budibase/budibase
- Overview: A platform for building custom business applications with database modeling, form generation, simple workflows, and AI-powered text/field generation.
- Target users: Small and medium-sized teams, lightweight internal systems, no-code enthusiasts.
- Use cases: Form apps, internal office tools, data-entry platforms. AI helps fill fields or create sample data, but complex logic support is limited.
refine
⭐ 33.1k
Website: https://refine.dev/
GitHub: https://github.com/refinedev/refine
- Overview: A React-based framework for building frontend admin panels with CRUD, permissions, and UI logic. Provides AI assistance for generating code and API bindings.
- Target users: Frontend developers and tech teams that need flexibility rather than drag-and-drop tools.
- Use cases: Admin dashboards and data tools where code-level control is required, but development speed is still important. Does not include backend modeling.
AI Workflow / Agent Orchestration
These tools stand out because AI is not just generating content — it actively participates in executing workflows. AI can call tools, trigger actions, and drive business automation.
Think of them as AI-enhanced workflow engines or agent execution platforms, rather than full low-code business system builders.
They excel at automation and execution, but usually lack complex data modeling, permission systems, or UI/page building capabilities.
n8n
⭐ 151k
Website: https://n8n.io/
GitHub: https://github.com/n8n-io/n8n
- Overview: Open-source workflow automation platform that lets you combine AI nodes with API workflows.
- Target users: Operations teams, support engineers, automation specialists, SMEs.
- Use cases: Automated content generation, data sync, email replies, AI-assisted decision workflows (e.g. AI replies to a customer → logs to database → sends email).
Dify
⭐ 117k
Website: https://dify.ai/
GitHub: https://github.com/langgenius/dify
- Overview: AI-native application and agent workflow platform with support for models, knowledge bases, memory, and tool calling.
- Target users: Product teams and developers building AI assistants, knowledge bots, or prototypes.
- Use cases: Knowledge-base chatbots, automated ticket handling, AI agents executing API actions.
Flowise
⭐ 46k
Website: https://flowiseai.com/
GitHub: https://github.com/FlowiseAI/Flowise
- Overview: Visual AI workflow builder based on LangChain, used to create RAG pipelines and conversational agents.
- Target users: AI developers, prototype teams, early-stage startups.
- Use cases: Knowledge Q&A, lightweight chatbots, multi-step reasoning demos — not suitable for full business systems.
Kestra
⭐ 22.7k
Website: https://kestra.io/
GitHub: https://github.com/kestra-io/kestra
- Overview: Backend workflow and data orchestration platform with AI Copilot for auto-generating workflows.
- Target users: Data engineers, backend developers, DevOps teams.
- Use cases: AI-generated scheduled jobs, data pipelines, API task chains — focused on backend execution.
Node-RED
⭐ 22.2k
Website: https://nodered.org/
GitHub: https://github.com/node-red/node-red
- Overview: Event-driven visual workflow tool widely used in IoT and system integration.
- Target users: Automation engineers, hardware developers, smart home/IoT teams.
- Use cases: Camera detects motion → AI makes decision → trigger switch; device anomaly → AI analysis → send alert.
Sim
⭐ 17.2k
Website: https://www.sim.ai/
GitHub: https://github.com/simstudioai/sim
- Overview: Designed for multi-agent collaboration, with visual building, execution, and API deployment.
- Target users: AI teams building multi-role agents, intelligent assistant startups.
- Use cases: AI assistants, automated report generation, intelligent task execution — though not as mature as n8n or Dify.
AI + Smart Spreadsheets / Database Tools
These tools don’t focus on workflow execution or full business systems — their core value lies in making data and spreadsheets smarter.
Here, AI is mainly used for generating, completing, querying, and analyzing data — not for handling complex business logic.
They can be seen as “Airtable / Notion Database enhanced with AI”, ideal for data-driven teams, content operations, or knowledge management — rather than heavy business systems or workflow automation.
NocoDB
⭐ 58.4k
Website: https://nocodb.com/
GitHub: https://github.com/nocodb/nocodb
- Overview: Open-source Airtable alternative that turns any database (MySQL, PostgreSQL, etc.) into a visual spreadsheet tool.
- Target users: Teams managing structured data — operations teams, internal collaboration groups, lightweight CRM users.
- Use cases: Content repositories, customer tables, inventory management, team collaboration sheets. AI features include field suggestions, content generation, auto-completion, and smart insights for data analysis.
Teable
⭐ 20k
Website: https://teable.ai/
GitHub: https://github.com/teableio/teable
- Overview: A PostgreSQL-based collaborative spreadsheet database with AI chat and smart autofill.
- Target users: Airtable/Notion users or teams needing data collaboration with AI-powered assistance.
- Use cases: Content management, lightweight data warehouse, team project tables. Supports conversational data interaction — such as querying records via natural language, generating table data in bulk, or auto-creating reports.
Tools That Don’t Fully Fit Into the Above Categories
During our research, we also found tools that don’t completely fall into any of the three main categories — such as Onlook.
⭐ 22.9k
Website: https://onlook.com/
GitHub: https://github.com/onlook-dev/onlook
What makes Onlook different is that its core capability is “AI-generated UI interfaces.”
With natural language or wireframes, AI can automatically generate page layouts, UI components, and even React code. These tools sit between design platforms and frontend low-code development, focusing on AI-assisted UI creation.
Tools That Span More Than One Category — Such as NocoBase
Earlier, we grouped tools into three types — business application builders, workflow/agent tools, and data/table intelligence platforms.
But in practice, some platforms span multiple categories — and NocoBase is the most complete and architecturally unified among them.
Unlike most tools, NocoBase brings together capabilities from multiple dimensions:
- Like a data platform, it provides database tables, fields, views, and APIs.
- Like a business system builder, it can generate pages, forms, permissions, page logic, and relational models.
- Through plugins and its open architecture, it can also extend into workflows, automation, and even embed AI nodes to execute parts of a process.
Of course, there are other tools with some cross-category features as well:
- Appsmith, ToolJet, Budibase — while mainly UI builders, they also support lightweight flows such as “button click → API → write data”.
- NocoDB, Teable — data-centric tools that now include AI autofill, webhook triggers, and basic rule automation.
However, these tools mostly add small AI features on top of their core functionality, making the experience smoother — but they do not form full workflow engines or complete system capabilities, and their extensibility is limited.
In contrast, NocoBase is not just “covering more areas”, but a true hybrid platform — starting from data modeling at the core, extending to pages and permissions, and further to workflows and AI via plugins. This level of architectural integration is rare among open-source low-code platforms.
Final Thoughts
We used to build systems by writing code. Then came drag-and-drop interfaces. Now, more and more people are starting to describe how a system should work — and let AI help build it.
“AI doesn’t remove the work — it just moves the work.”
The real challenge is no longer whether we are using AI, but whether we can deeply integrate AI with business logic, data, and workflows, and make it a part of the system itself.
AI is moving from a conversation tool to a construction tool. It isn’t perfect yet, but the direction is clear.
In the future, systems may not be “developed” — they may be described. And we will start from real business problems, not just from a code editor.
If you’ve read this far and feel the same shift happening, feel free to share this list with others who care about AI × low-code.👍
Related reading:
- Top 11 Open Source No-Code AI Tools with the Most GitHub Stars
- Top 18 Open Source AI Agent Projects with the Most GitHub Stars
- Top 20 Open Source AI Projects with the Most GitHub Stars
- Top 8 Open Source MCP Projects with the Most GitHub Stars
- Top 7 Open Source Web Applications with the Most GitHub Stars
- Top 40 Open-source Developer Tools with the Most GitHub Stars
- Top 15 Fastest-Growing Open-Source Low-Code Projects on GitHub in 2025
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