Kortix vs Flowise: a low-code LLM builder or an open-source platform your team owns

Flowise was the drag-and-drop builder that made LLM apps approachable, and it is now archived, which leaves open-source Kortix as the platform a team moves to when it wants to own the system. Kortix is the open-source AI Operating System: your agents, their skills, your company memory and every connector are files in one git repo you own, and each session runs on its own cloud computer. Flowise helped you draw a flow, while Kortix runs a workforce and lands its work for a person to review.

Compared here:

Flowise

What Flowise did well

Flowise is an open-source platform for building AI agents and LLM workflows, and its three visual builders (Assistant, Chatflow and Agentflow) covered everything from a first chatbot to multi-agent orchestration (Flowise docs).

• It started with a canvas. You dragged nodes for models, tools and retrieval instead of writing an orchestration layer, so a working RAG chatbot was minutes of clicking (Flowise docs).
• The node library was wide. Flowise connects to 100+ LLMs, embeddings and vector databases and builds on LangChain, so most model and store choices were a dropdown (Flowise).
• Self-hosting was one command. npm install -g flowise and npx flowise start run the whole builder locally, with a Docker Compose path for a server (Flowise on GitHub).
• A large community formed around it. Flowise built a substantial open-source following and a library of community templates before the project was archived, and the code stays on GitHub for anyone to fork (Flowise on GitHub).

Why Flowise is archived

Flowise's own announcement, The Future of Flowise, sets out the wind-down. Active development stopped on July 29, 2026, the GitHub repository moved to public archive in August 2026, and official end of life came on August 31, 2026. Issues and pull requests are locked, and the npm package and Docker images are marked deprecated (The Future of Flowise).

Flowise's team gave one reason for the shutdown: the shift toward coding agents. As models got better at reasoning, developers handed complex work to them, and the team says the rigid low-code workflow approach hits its limit at complexity (The Future of Flowise). The code stays readable: the core is Apache 2.0, with the enterprise directory under a separate commercial license (Flowise LICENSE.md). A team that built on Flowise can keep a fork alive, or move the work to a platform someone still owns and maintains.

One repo your company owns

Kortix takes the route a canvas cannot. The agents, the skills they share, the company memory, the connector configuration and the triggers are files in one git repo you own, so the whole system is versioned, diffable and readable by anyone on the team (Kortix on GitHub). Agents are markdown, and kortix.yaml declares the machine image, the connectors and the triggers, which makes a change to how the company works a diff a reviewer reads.

Every session runs on its own isolated cloud computer, on a branch named after the session. The agent can install packages, run code and break things inside that machine, and only what it commits survives (Kortix on GitHub). Session work reaches main through a change request a person reviews, and merge is default-deny for agents (Kortix docs).

Kortix is model-agnostic. Pick the model per agent, per session or per message, bring any model provider with your own keys, or connect the ChatGPT plan you already pay for (Kortix docs). It reaches 3,000+ apps plus any MCP, OpenAPI, GraphQL or raw HTTP API, and connector credentials are brokered server-side and never enter the machine (kortix.com). You set each tool call to allow, ask or block, and approval gates are off until you switch them on.

Kortix deploys on Kortix Cloud, in your VPC, or on your own on-prem network, and self-hosting is free. Free is $0 with 200 credits a month and 1 project; Team is $40 per seat per month with 2,500 pooled credits per seat (Kortix pricing).

Where Flowise still fits

Flowise did its job well, and for some teams a fork is the right answer. If you already run a Flowise instance and the work is a short, deterministic chain on your own machine, the Apache 2.0 core is yours to keep running, and a canvas still shows that chain at a glance (The Future of Flowise). The catch is what a fork carries: security patches and dependency drift with no upstream. Flowise also keeps its configuration in its own database and its memory with the flows you build, so it was built for drawing steps, which leaves governing a workforce outside its design (Flowise docs).

Kortix and Flowise side by side

When to pick which

Next reads: Kortix vs n8n makes the same case for fixed workflows, the self-hosted AI workspace roundup compares six platforms by memory and approval, and how to choose an open-source AI agent platform covers the wider category.

Free to start, free to self-host. Get started with Kortix and bring one job your team already runs.