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There is something about agent workflow builders not gaining as much traction as they could have. Open AI also launched something in house and they are also shutting it down
OpenAI is deprecating Agent Builder. Existing users can continue using it during the transition window, and the product is scheduled to shut down on November 30, 2026. ChatKit remains available. See the deprecations page for the current timeline.
There does seem to be a need for a semi-deterministic workflow where nodes in the workflow are agentic, i.e., powered by LLMs, but the workflow itself remains deterministic. Lets say my daily workflow is to download a PDF from some website, extract some information from it, and email that information. I cannot just ask an agent to script this workflow, because the website will keep changing, the PDF layout will keep changing, etc. I need a "download agent", an "extract info" agent, and so on which will examine the website, PDF, etc. and generate any one-off scripts everytime the workflow is run.
I guess the tradeoff is doing it in a workflow builder vs just dumping all your requirements in a single agent, With LLMs getting smarter, they can create sub agents automatically if required and take the abstraction away from the user.
The problem that workflow engines solve (configurable code) is just better solve with agents just maintaining the code.
2 years ago (during the agentic workflow boom) prompting was annoying and tedious. Context was small, harnesses were weak. You needed to string along multiple complex prompts to get anything done.
Now, you can give an agent 50+ tasks and it will churn through them.
I‘m wondering what that means for products like retool. I used it a lot in the past for internal tooling but it’s now a a segment where coding agents are likely to be a better fit, and the AI integration within retool is messy compared to just letting Claude create the web app
Both cases same story: two years from now someone's on-call getting paged because a workflow node silently changed behavior and nobody remembers why it was built that way. Deterministic code doesn't do that.
>Over the last few months, we've noticed a significant shift in how people build. As AI models become more capable at reasoning, we've noticed that developers are increasingly relying on new coding agents such as Claude Code/OpenClaw to handle complex tasks. The typical rigid workflow low code approach quickly hits the limit when it comes to complexity.
Their front page says ‘design agents visually’ so their target market was less skilled devs/non devs.
So not sure why they are giving the reason if closer as cli related when low code and cli are different markets.
Sure, but the point stands either way. As far as I can tell the shift Flowise is describing isn't CLI vs GUI, it's whether the interface exposes a fixed graph or lets the model plan its own steps. Desktop wrapper doesn't change that underlying architecture.
I feel like there's a fundamental shift in the user interface that's causing this. People are starting to expect an "agentic interface" - that they are able to give kinda vague inputs and nudge things to work in a way they like, over the traditional experience of having increased clarity at the cost of increased setup (drag and drop, many UI first interfaces, etc).
I was editing a google doc yesterday and found it annoying that i had to find the "link" option from the context menu instead of just saying "turn links into links" and moving on.
This imo impacts everyone, regardless of how developer-ey you are. Perhaps non-devs expect a "friendlier"/more "agentic" interface now? Don't have the right vocabulary to explain.
Yep, most modern workflow and agent builders now support editing via natural language. Mastra even has an agent that helps you build other agents. An agent-builder agent, if you will.
Full disclosure I'm a founder of cbk.ai and chatbotkit.com, where we've built reusable agent components. I have never really saw flowise, n8n the now defunct OpenAI agent builder and other similar products as competitors and I wanted to describe why.
I think workflow builders are mostly the wrong mental model for AI. A graph of predefined nodes and edges is useful when you already know the exact sequence of work.... but the point of an AI agent is that it can decide what to do next from the current context.
IMHO reusable building blocks still matter enormously. You should not have to recreate an integration, a capability, or a piece of reliable deterministic logic every time. It is wasteful and you cannot oneshot it. Even with coding agents which we use all the time system to system connectivity is more complex than writing the code. But those should be available to the agent as tools, mechanics, and features that are not wired together as a visual workflow of sorts.
A product can look graph-like at a glance though, while avoiding making a workflow builder. I think this still matters in order to put a mental model around it. But the mechanism should not be graph / workflow like in practice.
I think you tend to need both. There's certain deterministic data and steps that surround the agent as well as certain agentic steps where the LLM is controlling the execution flow.
n8n had an LLM step, not sure if they had an "agent" step. For instance, your workflow could pull down Google Docs using their connectors and deterministic steps then run something agentic with the contents, then upload the contents to Google Drive or send an email.
"Over the last few months, we've noticed a significant shift in how people build. As AI models become more capable at reasoning, we've noticed that developers are increasingly relying on new coding agents such as Claude Code/OpenClaw to handle complex tasks." is true when your primary users are developers. But no-code workflow is still valuable at scenarios where determinism is required.
I'm building an agentic compliance platform where every procedure needs to follow company policies and operated on the rigid plan, but the procedure itself is different for every customer. Coding agents could in principle generate the necessary workflow for each company, but they need to integrate with millions of other applications in the company, easy to review and modify.
We built our own workflow builder because every other existing solution is too complicated and doesn't meet compliance requirements. The workflow builder itself was coded by Claude, and it's working wonderfully.
So flowise's shutdown is because their business is targeting a specific sector which doesn't need it anymore, not because workflow builder itself is not useful.
I remember Flowise app being well-intended but bad. I evaluated the self-hosted version to be used as a component in an internal tool so non-technical people can tweak their prompt chains without writing code. As far as I remember:
1. it became increasingly slow as I added flows (felt like a O(n^2) ish bottleneck)
2. There were many bugs and bad UX
3. the flow variable system was underbaked and underdocumented
4. feature gaps
I decided I was probably not the target audience for their app and moved on.
There's this magnetic appeal to use a visual graph editor as the UI paradigm for your app.. it seems to fit so many problems well.
Yet if you complicate the problem just a notch, the graph becomes big fast and that renders like complex spaghetti you're afraid to touch in the UI.
The other problem is that the UI paradigm becomes the driver - you try to think of the problem in terms of fitting it into the UI graph editor, which is often not a natural way of thinking about said problem.
All of this to say is that I've always thought that graph/flow editors looked cool, except I've never found good use for them in practice except for very simple and predictable graphs.
Just writing the flow as a data structure in text is often a lot simpler and practical..
Flowise was acquired by workday, so I'd take this with a grain of salt.
However, I agree with the reasoning. I don't see a future for visual workflow builders. They are used to define plumbings that can be defined by a 50 line python script.
A non-technical user does not need to draw this workflow. They can just ask an agent to write that code for them, to debug it, to visualize it. All workflow builders now have an AI chat where you ask the agent to build the flow for you. Why not skip the flow part?
I'm pretty biased though since that's the thesis behind what I am building.
I would counter and say it’s understanding that makes the difference.
Having an AI generate 50 lines of code that you assume will do the job is a little different to creating a flow that you understand and maintain.
AI is really good at making everything seem so simple and easy, until it breaks. But then again, just use another AI to fix it.
So then there is an agent orchestration layer for coordinating multiple agents with multiple personalities, role playing multiple parts in a comedy called software development.
We still don’t know what they are really doing but does that matter? Does knowledge, experience, learning and failing even matter if AI is doing it for us?
Disagree that "understanding" and "AI wrote it" are opposites. Ask the agent to explain the flow back to you in plain english, same understanding, less time spent dragging boxes around a canvas.
Deterministic workflows still have plenty of use cases. AI agents can help build them, but can’t replace them when predictable and auditable execution matters, because agents are inherently nondeterministic.
That said, Relay.app announced its shutdown a few weeks ago, and now Flowise is winding down too. I wonder whether coding agents are really the cause, or whether these products simply struggled to find a sustainable market.
Disclosure: I’m building Aqua Blue (aquablue.app) around a related idea: AI builds the workflow, but fixed rules run it.
"Visual graph editor for AI workflows" seems like an extremely saturated market (Langflow, n8n). Consolidation seems natural as the agent engineering industry matures.
With the speed everything is going now, nobody has time to learn nice visual tools. They set their AI agents an objective and leave them to iterate until the objective is reached.
as a person who spent years building flow.ai before flowise.ai, i'd say drag n drop ui was dead on arrival it works for a set of very hand picked cusomers , the rest were in a weird spot between people who can code( and think accordingly ) and don't.
You hit the nail on the head. A couple years ago I started prototyping a similar node based AI workflow builder which worked well but everyone I showed it to was either; confused at how to use it (even for super basic stuff), or liked it but poked holes by asking "can it do x, can it do y" which it couldn't. There is such a small niche of people technical enough to use something like this that wouldn't just opt in for coding it themselves (especially with AI assisted coding nowadays)
I do still use a part of it on one of my projects for building multi-step image processing pipelines for AI property photo editing (ffocal.io for the curious) that are configurable in the admin panel and it works super well and is useful for modifying and testing pipelines. So there are use cases, they're just very narrow
It lacked so many features that n8n and Langflow offered, so I'm not sad about it, but it was the first visual agentic workflow builder that I tried and I wish they could make it somehow.
Whilst the UI provides visibility and guardrails, it also reduces flexibility. But now with LLM coding agents, it often gets in the way. Working with text/code is easier.
Trying to make an harness and not falling behind with how fast those frontier model evolve is hard. We've built our own harness to make games, but it became obsolete as soon as a new model dropped. Gave up and now just letting user use whatever harness they want on their side.
Disclosure up front: I build LLMGraph (llmgraph.ai), a hosted product in the same category, so I have a horse in this race.
The interesting part of the announcement is the stated reason: "developers are increasingly relying on new coding agents" and "rigid workflow low code approach quickly hits the limit when it comes to complexity." I think that's half right. Coding agents are clearly eating the developer end of this market. But a lot of Flowise usage was never developers; it was teams who wanted to design an LLM pipeline visually, hand it to a non-engineer to tweak prompts, and deploy it without owning infrastructure. That need didn't go away this week, and telling those users to fork and maintain a large TypeScript codebase themselves is not a real answer for them.
For anyone running Flowise in production, the practical decision is fork-and-maintain versus migrating somewhere maintained. Forking is a bigger commitment than it sounds: you own dependency updates, security patches, and model API churn indefinitely, and the contributor community that used to absorb that work is dispersing. Worth being honest with yourself about whether your team will actually do that maintenance before the first CVE forces the question.
Ran into this with n8n self-hosted stack, honestly just moved everything to plain scripts calling the model APIs directly with a thin state machine. Took a weekend but now nothing depends on a company staying alive. Visual builders are great for prototyping, terrible for anything you actually need running in a year.
https://developers.openai.com/api/docs/guides/agent-builder
OpenAI is deprecating Agent Builder. Existing users can continue using it during the transition window, and the product is scheduled to shut down on November 30, 2026. ChatKit remains available. See the deprecations page for the current timeline.