10 Flowise Alternatives After the Shutdown, 9 Tested by Hand

Flowise shut down on August 31, 2026. If you built anything on it, you must be migrating to another right now.

I spent seven days rebuilding the same small AI agent, a customer-support and lead-intake agent for a fictional coffee-box subscription brand, in every tool I could build hands-on.

By the end, you'll know what each tool does well, where it falls short, who it's actually built for, and what it costs, so you can pick without running this same test yourself.

TL;DR

1. Lindy: Best for a non-technical operator who wants a finished agent handling customer support and lead intake across channels.
2. Langflow: Best for developers who want a free, open-source visual canvas built on LangChain.
3. Dify: Best for teams that want governance, analytics, and prompt tracking built in from day one.
4. n8n: Best for technical teams that want the agent wired into a bigger automation stack.
5. Voiceflow: Best for teams designing a customer-facing chat and voice experience.
6. LangGraph: Best for engineers who want full code-level control over agent logic.
7. StackAI: Best for regulated teams that need enterprise-grade RAG and compliance.
8. Relevance AI: Best for teams ready to build a multi-agent workforce themselves, starting from its template library.
9. Make: Best for teams that want a large integration library with visual, credit-based control.
10. Zapier: Best for teams that already live inside Zapier's app ecosystem.

How I tested these Flowise alternatives

For nine of the platforms, I ran the identical brief:

Build an AI agent that handles customer support and lead intake for a small subscription coffee-box brand, a test case, never a real business. It should answer using only three uploaded documents (a FAQ, a shipping policy, and an ingredients list) and never invent an answer that isn't grounded in them.

Keep every answer under 75 words so it reads like a real chat reply, not a policy page. If the same visitor asks the same question twice in one session, the agent should recognize the repeat instead of searching the documents again from scratch.

If someone says they want to cancel their subscription, the agent should never try to talk them out of it or offer a discount. Instead, it should stop, tell the visitor a teammate will follow up, and post the last three messages to a Slack channel for a human to pick up.

The same agent runs on a website chat widget, with escalations posting to a Slack channel for a human to pick up. If a visitor submits their email twice in one day, the connected spreadsheet should update that one row instead of adding a duplicate.

Tell me whether a non-technical teammate could edit one answer without touching code, and what going live would actually take, since I'm not doing that here.

Note: LangGraph is a toolkit for programmers, not an app to click around in, so building this brief manually would have taken longer and needed more coding skill than any other tool here. I used its documentation instead, and since it's free and open source, you can run the same test yourself.

I started with 19 tools and cut nine before writing a single section:

• Gumloop is built as a broader agent-and-workflow platform for sales, support, and ops. That's a different job than the visual LangChain-style canvas Flowise alternatives are built around.
• Relay.app was ruled out early on for not being built for low-level LLM or vector-store control. It shut down in September 2026 anyway, so it's out.
• Dust has no self-host option, a real departure from what makes Flowise appealing, and its per-seat pricing scales awkwardly for a build this size.
• Sana positions its agents around internal company knowledge work, like sales prep and CRM updates pulled from a team's own connected apps, a different job than this brief's document-grounded, customer-facing conversation.
• Copilot Studio is built around the Microsoft 365 and Azure ecosystem, fine if you're already there, a real drawback if you're not.
• Typebot is built for chat and lead-capture, a narrower job than general agent orchestration.
• Budibase now lists agents alongside apps, automations, and a service portal as its four core products, but its agents answer employee questions and handle internal requests. That's an internal, back-office use case, not what this brief was built to test.
• ZenML serves engineering teams building and tracking machine-learning pipelines, a different audience than someone building a conversational agent.
• Dynamiq's case for a customer-facing build rests on its own vendor comparison page, a single self-reported data point, not on anything in its public repository.

Why look for Flowise alternatives?

Flowise's own team announced in July 2026 that it would shut down. The software itself still runs, since it's open-source, but nobody's maintaining it anymore.

That means tangled multi-step tracking, basic debugging, and thin cost visibility are staying exactly as they are, since there's no team left to fix them.

Which Flowise alternative should you choose?

Match the tool to the job it's built for:

• Choose Lindy if you want a finished agent that runs itself across Slack and channels like email or text, and that a non-technical teammate can keep updated without touching code.
• Choose Langflow if you want a free, open-source LangChain canvas that shows you exactly what's happening inside every node as it runs.
• Choose Dify if you want governance, analytics, and prompt tracking built in without assembling them yourself.
• Choose n8n if this agent needs to run on a schedule, react automatically when another app sends it an update, and plug into the rest of your operational workflow, reaching further than one chat window.
• Choose Voiceflow if the job is a customer-facing conversation across chat and voice, with real compliance certifications behind it.
• Choose LangGraph if you want full code-level control of agent logic and already have the Python or JavaScript skills to write it directly.
• Choose StackAI if you're a regulated team that needs enterprise-grade RAG, SSO, and on-prem or VPC hosting past the working-demo stage.
• Choose Relevance AI if you want to build a multi-agent workforce yourself, starting from its template library instead of a blank canvas.
• Choose Make if you already run visual, flowchart-style automations with branching logic and want to add an agent step to that same setup.
• Choose Zapier if you're already using apps Zapier integrates with and want the deepest integration library on this list.
• Stick with a Flowise fork only if you're comfortable self-hosting and keeping the code up to date yourself. The core team no longer patches bugs or ships new features. Treat this list as your migration plan.

10 best Flowise alternatives: At a glance

The 10 best Flowise alternatives

1. Lindy: Best for a non-technical operator running the finished agent

Lindy built the test agent from plain English in three hours and 40 minutes, getting the document grounding and the word cap right from a single instruction. The one place it stumbled was duplicate emails.

Lindy doesn't offer an embeddable website chat widget, so the website half of this test ran through Slack instead; in production that would be a shared support inbox, a business line, or a lead-capture form feeding into Lindy.

By default, it adds a new spreadsheet row instead of updating one, though a quick fix for that sits in the Skills library.

This is the closest tool here to a finished agent a non-technical person can run. Describe what you need in plain English, and it runs across text and Slack with no code involved. 

If your team isn't technical and just needs it working, Lindy is the pick; for hands-on control over every step, look elsewhere on this list.

Key Features

• Plain-English instructions: Describe what Lindy should do, and it handles the work from there.
• Slack-native access: Teammates can mention Lindy directly in a channel or thread, and it carries context from other conversations into Slack automatically.
• Skills library: Prebuilt Skills cover common support, scheduling, and outreach patterns, so builds don't have to start from scratch.
• Human-in-the-loop approvals: In shared Slack threads, writes like canceling an order can require a teammate's approval before they happen.

Pros

• ✅ A non-technical teammate can edit how the agent answers one question without touching code.
• ✅ Carries context cleanly within Slack on the same tasks.
• ✅ 1,500+ integrations meant the Slack escalation and spreadsheet update didn't need custom integration work.
• ✅ No credit card is needed to try it, so testing the agent costs nothing upfront.

Cons

• ❌ By default, it adds a new spreadsheet row every time someone submits their info, instead of updating the row that's already there, so catching repeat emails takes one extra step to set up.
• ❌ The $50 in starting credits lasts only seven days, so it's a trial window rather than an ongoing free option.
• ❌ It isn't built for hands-on control over every step of a workflow.

Best For

• A support or lead-intake agent that a non-technical operator needs to run and edit themselves.
• A team that wants one agent working across Slack and other channels without having to code them together.

Pricing

A Free plan exists; no credit card is required, with $50 in credits to start. Setup takes about two minutes, and those free credits last seven days. After that, you pick a paid plan to keep going.

Team runs $49.99/month per active user for 5,000 credits per user per month, more than enough for a build this size.

Enterprise adds HIPAA compliance with a signed BAA, SSO and SCIM, audit logs, shared usage with bonus credits, dedicated support, and onboarding and enablement, priced by talking to sales.

There's no annual pricing. Team is billed monthly, per active user.

2. Langflow: Best for developers who want a free, open-source canvas

Langflow took six hours and 10 minutes to build the same agent, the slowest of any tool here.

Its real edge is watching data move through every node as it runs, so a bad answer is easy to trace. 

It can't hand off to a non-technical teammate, since getting a flow live still takes a developer. Good for a team with engineers to finish the job, not one without them.

Langflow is also still actively maintained, with version 1.13.1 as its latest release. Most of its recent releases are security patches rather than new features, exactly what you want from something you're self-hosting.

Key Features

• Node-level data inspection: See the exact data flowing through every node as a flow runs, which makes tracing a bad answer fast.
• Full LangChain compatibility: Works with any LLM, vector database, or embedding model in the LangChain ecosystem.
• Free, self-hosted or local: Run it on your own infrastructure at no cost, or use Langflow Desktop to run it locally.
• Exportable endpoint: Every flow becomes callable once it's built.

Pros

• ✅ The closest jump from Flowise, with the same visual, LangChain-based approach.
• ✅ Free and open-source, with no usage limits to plan around.
• ✅ Retriever, memory, and branching pieces come built in, so most agent logic needs no custom code.
• ✅ An open-source project under active development is a real advantage once you've watched Flowise's own team stop shipping.

Cons

• ❌ Getting a canvas into an actual multi-channel launch, a real website widget plus Slack, needs engineering work beyond the builder itself.
• ❌ No hosted cloud option exists anymore, paid or free; running it means self-hosting or using Langflow Desktop locally.
• ❌ Slowest of the nine hands-on builds, at six hours and 10 minutes.

Best For

• A developer who wants a free, visual way to try out LangChain-style agent logic before writing it by hand.
• A team that already has engineering resources to take a working canvas the rest of the way to production.

Pricing

Langflow itself costs nothing. The core framework is open-source and free to self-host or run locally with Langflow Desktop. 

DataStax shut down its hosted Langflow service on April 9, 2026, so there's no official hosted cloud option today. Running it now means self-hosting it, anywhere from a basic Docker setup to a full Kubernetes deployment, or running Langflow Desktop locally instead.

3. Dify: Best for built-in governance and observability

Dify was the fastest build on this list, clearing the whole brief in one hour and 55 minutes. The cancellation rule needed one extra branch node to pass.

Slack itself connects through Dify's Slack Bot extension, which means creating your own Slack app rather than a native toggle.

A non-technical teammate could edit any part of it without touching the setup underneath. Dify trades a blank canvas for built-in guardrails, like logging and analytics on by default. That makes it the strongest pick if you want speed and governance without assembling it yourself.

Dify's cloud also includes a free option with 200 message credits, a lighter entry point than the paid plans above. The open-source version stays free to self-host too, for anyone who'd rather skip the cloud entirely.

Key Features

• Built-in observability: Prompt tracking, analytics, and execution logs ship on by default.
• Conditional branch nodes: Route the conversation down different paths, like an escalation rule with a built-in exception, using nodes already on the canvas.
• One-box platform: Prompt design, RAG, tools, and hosting all live in one platform, already connected to each other.
• Cloud or self-hosted: Run it managed or on your own infrastructure depending on your compliance needs.

Pros

• ✅ Built-in branching handles rules with exceptions, like escalating a cancellation, without custom code.
• ✅ Built-in branching and conversation memory mean a working agent comes together fast.
• ✅ Edits to how the agent answers don't require touching anything underneath the setup, even without a technical background.

Cons

• ❌ Dify's built-in structure gets in the way if you want full, from-scratch control over every technical detail.

Best For

• A team that wants governance and observability without assembling it from separate tools.
• A support or lead-intake agent that needs conditional logic, like an escalation rule, handled cleanly.

Pricing

Dify's paid cloud plans start at $59/month per workspace for Professional, with Team at $159/month per workspace and custom Enterprise pricing.

Dify Cloud also has a free Sandbox plan with 200 message credits. Self-hosting is also available for teams that want to run it on their own infrastructure instead.

4. n8n: Best for teams that want the agent wired into automation

n8n treats the whole agent as one step inside a bigger workflow, and it cleared the whole brief in two hours and 15 minutes.

The trickiest test was the spreadsheet: if the same visitor submitted their email twice in one day, it had to update that row instead of adding a duplicate. n8n's Google Sheets node caught that natively, with nothing custom to build.

n8n's real strength is reach. Webhooks, cron jobs (scheduled, time-based triggers), and Slack messages can all feed into the same workflow, though editing it safely still takes real workflow knowledge, not a non-technical skill.

Key Features

• Native execution triggers: Webhooks, cron schedules, and app-specific triggers (Slack, email, and more) work out of the box.
• Google Sheets Append or Update Row: Handles exactly the duplicate-row problem this brief was built to catch.
• Self-hosted or cloud: Run it on your own servers for free, or use a managed cloud plan.
• Custom code steps: Drop in JavaScript or Python when a node doesn't cover what you need.

Pros

• ✅ The Google Sheets node can update an existing row instead of adding a duplicate.
• ✅ Free to self-host, with a low-cost cloud tier for teams that don't want to manage infrastructure.
• ✅ This agent plugs straight into n8n's existing automation stack, not just the chat brief it was built for.

Cons

• ❌ Memory isn't on by default and needs a memory node added, though most of the other tests passed on native settings alone.
• ❌ Self-hosting it at scale adds infrastructure cost on top of your existing hosting bill, a surprise once usage climbs. 

This n8n alternatives roundup is worth a look if that execution model is the problem.

Best For

• A team that wants the agent wired directly into a larger operational workflow.
• Anyone who needs real de-duplication and cross-app logic more than conversational polish.

Pricing

n8n pricing starts at $24/month for Starter (2,500 workflow executions/month).

Pro runs $60/month (10,000 executions), and Business runs $960/month (40,000 executions, self-hosted only at this tier).

Enterprise is a custom quote for a custom number of executions, hosted or self-hosted. On Business, workflows keep running past the included executions, but overage charges apply if you don't upgrade: $4,800 (€4,000) per extra bucket of 300,000 executions. Starter and Pro are capped and have no overages.

5. Voiceflow: Best for a customer-facing chat and voice experience

Voiceflow took two hours and 40 minutes to build the brief, and didn't clear all of it.

Document grounding (keeping answers limited to the uploaded reference documents instead of letting the model improvise) worked out of the box. Slack isn't a built-in Voiceflow channel, so getting the agent to run there and routing the cancellation handoff into it both needed a custom integration: Voiceflow's own Dialog Manager API, wired up to a Slack app you build yourself.

You also won't see a price until you create an account, though Pro and Business upgrade in-app and only Enterprise goes through a sales conversation.

Voiceflow reports over 3,000 customers and more than 1 billion messages processed through the platform.

Key Features

• Knowledge Base grounding: Upload source documents and the agent answers only from what's there.
• Workflow Builder and Playbooks: Scripted steps for logic that has to run the same way every time, plus a natural-language mode for parts of the conversation where the agent needs to reason on its own.
• Voice and chat in one build: The same flow can run as a website chat widget or a voice interface.
• Function steps: Drop in custom calls to outside services when a prebuilt step doesn't cover what you need.
• Multiple model choice: Pick from OpenAI, Anthropic, and other providers per agent.
• Enterprise compliance: SOC 2 Type II, ISO 27001, GDPR, and HIPAA compliance, with SSO and permission controls for team access.

Pros

• ✅ The visual flow builder itself is intuitive, even with a fairly complex document-grounded flow.
• ✅ Answers stayed grounded to the three documents and under 75 words.
• ✅ A customer base in the thousands and well over a billion processed messages back up just how proven this platform is.

Cons

• ❌ Prices only show up after you create an account, so you can't compare costs before signing up. 
• ❌ Complex flows can take a moment to load during editing.
• ❌ Slack isn't a built-in channel, so running there and connecting it to the website chat both need custom work.

This Voiceflow alternatives roundup is a better starting point if pricing transparency matters to you upfront.

Best For

• A team building a customer-facing support or lead-intake conversation across chat and voice.
• A build where document-grounded, tightly-worded answers are the priority over back-office automation.

Pricing

Voiceflow pricing stays hidden until you create a free account. Once signed in, Pro runs $60/month and includes $60 in monthly credits.

Business runs $150/month and includes $150 in monthly credits. Enterprise pricing and its credit allowance are set by sales.

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6. LangGraph: Best for engineers who want full code-level control

LangGraph wasn't built for this test. It's a free, open-source code library, not an app with a signup flow, so there was no agent to run the brief against. 

This is the most hands-on, code-only option here. You write the agent's logic yourself in code, with no visual builder to lean on. That means real Python or JavaScript skills on your team, though its GitHub project has crossed 42,000 stars, the clearest sign of how widely it's trusted.

LangGraph itself is free and MIT-licensed. Its hosted deployment layer now lives inside LangSmith, next to observability and evaluation tools, if your team wants to grow into those later.

Key Features

• Full code-level control: Define agent state, edges, and logic directly, with no visual abstraction layer between you and the behavior.
• Persistent memory: Built-in support for storing conversation history and context across sessions.
• Human-in-the-loop hooks: Add moderation or approval checks at any point in an agent's execution.
• Model-agnostic: Works with any LLM provider, and you can swap providers at any time.

Pros

• ✅ Completely free and open-source, with no account or signup required at all.
• ✅ The most control of any tool on this list over exactly how an agent behaves.
• ✅ Over 42,000 GitHub stars put this well ahead of most alternatives here in community adoption.

Cons

• ❌ Requires real Python or JavaScript fluency. There's no visual builder to fall back on.
• ❌ More of the structure is on you to build yourself once a requirement moves outside standard patterns.

Lindy's LangChain alternatives roundup is worth a look if writing LangChain code yourself isn't where you want to spend your time.

Best For

• An engineering team that wants full code-level control and is comfortable owning that complexity.
• A standard RAG or tool-calling pattern, where LangGraph's structure already fits cleanly.

Pricing

LangGraph itself is free, with no account, subscription, or usage limit. Install it with one command: pip install langgraph. LangSmith, the same company's companion product, has a free Developer plan with observability included. Hosting and deployment start on the $39-per-seat Plus plan.

7. StackAI: Best for enterprise RAG and compliance

StackAI is built for teams whose RAG setup has to pass a compliance review. Asana acquired it in May 2026, though StackAI continues to operate as its own product and brand. The build took three hours and five minutes and cleared the whole brief.

Repeat-question recognition passed once Conversation Memory was switched on, so it's worth the learning curve if enterprise compliance and self-hosting matter to you.

StackAI's own announcement promises "shared memory, goals and context" with Asana going forward, but that's vendor framing. It also advertises 500+ enterprise integrations, including the Slack Bot plugin covered below.

Key Features

• Native Slack Bot plugin: Run the same agent on a website widget and Slack without custom integration work.
• Enterprise compliance: SOC 2 Type II, ISO 27001, HIPAA, and GDPR.
• Deployment options: On-prem (installed and run on your own servers, not StackAI's cloud) and VPC hosting on Enterprise.
• Knowledge base and document loaders: Ground answers in uploaded files, web scraping, or connected drives.
• Stacked workflows: Build smaller flows and connect them into a larger master flow, one manageable piece at a time.

Pros

• ✅ The free plan's 500 runs a month is enough to build and test an agent before paying.
• ✅ A single settings change (switching on Conversation Memory) was enough to fix the repeat-question gap entirely.
• ✅ The 500+ integrations on offer go far past what this one build needed, with room to grow into later.

Cons

• ❌ Complex workflows can make the visual interface crowded and errors harder to trace.
• ❌ There's no self-serve tier between Free and Enterprise: outgrow the 500 monthly runs, two projects, or one seat, and the only option left is a custom quote.

Best For

• A regulated team that needs RAG with real compliance controls, past the working-demo stage.
• A build that benefits from chaining several smaller flows together.

Pricing

StackAI's Free plan costs $0/month with 500 runs a month, two projects, and one seat. Beyond that, Enterprise is a custom quote. There's no self-serve paid tier in between the two.

What happens once those 500 monthly runs are used isn't stated anywhere in StackAI's own material. No overage rate, hard stop, or upgrade prompt appears on the pricing page, in the plan-comparison table, or in the documentation.

8. Relevance AI: Best for a multi-agent "workforce" with rollout support

Relevance AI's core pitch is multi-agent "workforces." Support runs through its community forum rather than a dedicated account manager. The build took four hours and 20 minutes, one of the longer ones tested.

Document grounding worked once the right settings were found, though duplicate-email handling and the non-technical hand-off weren't conclusively confirmed either way.

Pricing runs on Actions plus Vendor Credits, starting at $29/month for Pro. A thin agent library or that pricing model is reason enough to browse Lindy's Relevance AI alternatives roundup instead.

Relevance AI has since pushed its signature feature to "Invent 2.0," now billed as a "24/7 AI solutions engineer." The core pitch hasn't changed. A guided rollout team gets you live, then teaches your team to build on their own.

Key Features

• "Invent" feature: Describe what you want in plain language, and it suggests tools and implementation steps.
• Multi-agent orchestration: Run specialized agents for different functions that coordinate with each other.
• Broad integration library: Connect to 2,000+ apps and APIs without custom code.
• Self-serve account creation: A real signup exists even though the public site is built around demo-led onboarding.

Pros

• ✅ The natural-language Invent feature cut through the early confusion by suggesting the steps itself.
• ✅ Getting document grounding and cross-channel memory working just took finding the right settings, not custom engineering.
• ✅ You're not left alone with a blank canvas here. Its template library and Invent feature give you a working starting point to build from.

Cons

• ❌ Onboarding is confusing at first, and self-serve prices sit in the docs instead of the main pricing page, so both take real time to track down.
• ❌ The prebuilt agent library is thinner than the marketing suggests. Several pieces had to be built from scratch.

Best For

• A team that wants to build its own multi-agent setup from templates, without needing Enterprise-level support to do it.
• Multi-agent coordination that spans several business functions at once.

Pricing

Relevance AI's pricing page only shows Enterprise, but its docs list self-serve plans: Pro at $29/month and Team at $349/month.

Both are billed in Actions plus Vendor Credits. The free plan is retired, and Enterprise is a custom quote.

9. Make: Best for a large integration library with visual control

Make sits in the broader AI automation software category and layers an agent step on top of its usual scenarios, and it cleared the whole brief in two hours and 50 minutes.

Everything inside Make worked through visual modules.

The website chat window itself sits outside Make and feeds it through a webhook. The one real catch is non-linear billing.

Credits can spike fast at scale, and other users have had a runaway scenario burn through theirs with no warning (one reported watching their operations grind to a halt until support stepped in).

Make itself has a similar ownership story to a few other tools on this list. Celonis, the process-mining company, acquired it in 2020 (back when it was still called Integromat) for more than $100 million.

It kept running as a standalone product afterward, rebranding to Make in 2022 rather than disappearing into its parent company.

Key Features

• Router and filter modules: Handle conditional logic, like escalate-versus-continue, using the same visual modules as everything else in a scenario.
• 3,000+ app integrations: Connect Google Sheets, Slack, and most common business tools natively.
• Visual scenario builder: See the entire automation laid out as a flowchart, every branch visible at once.
• AI Agents: Build and manage agent logic inside the same scenario canvas.

Pros

• ✅ A non-technical teammate could edit any module from the visual scenario view.
• ✅ Native modules cover branching, de-duplication, and Slack without custom code.
• ✅ Every step runs through a visual module you can click into and follow, so nothing about how the agent decided something is a mystery.
• ✅ An acquisition didn't kill this one. Make outlasted its 2020 buyout and is still shipping under its own name, unlike Flowise's own team.

Cons

• ❌ Credits don't scale in a simple, predictable way once a workflow has several steps, a surprise risk at higher volume.

This Make alternatives roundup covers tools that price workflow automation differently, if that credit model is the blocker.

Best For

• A team that already runs automation scenarios in Make and wants to add agent logic to an existing workflow.
• A build where visual, module-based logic is easier to update than a code-first approach.

Pricing

Make's Free plan comes with 1,000 credits a month, fixed. Core, Pro, and Teams all share the same credit slider, from 10,000 credits up past 8 million. The price scales with whichever tier you pick.

At the 10,000-credit mark: Core starts at $16/month, Pro runs $28/month, and Teams runs $51/month.

Enterprise is a custom quote. It adds overage protection the paid tiers don't include.

10. Zapier: Best for the deepest app integration library

Zapier's advantage is the size of its integration library, so the Slack and spreadsheet steps here were standard, well-documented actions. It took two hours and five minutes to build, and cleared the whole brief.

Its new AI steps also default to a pricier model tier, so budgeting an AI-heavy workflow here takes more care than it used to.

 The newer push is MCP and SDK support, so teams can call Zapier actions straight from an AI coding agent.

Key Features

• 9,000+ app integrations: The widest library of any tool on this list, by a wide margin.
• Paths (conditional branching): Route a conversation to escalation or continuation without custom logic.
• AI-powered steps: Add AI actions inside a standard Zap without switching platforms.
• A separate Zapier Agents product: A standalone line for building more autonomous AI teammates, billed apart from the core automation plan.
• Visual builder: Built for non-technical users from the ground up.

Pros

• ✅ That breadth in integrations meant almost nothing needed a workaround.
• ✅ Paths handles branching logic visually, with no code.
• ✅ Zapier actions aren't locked to the Zap interface anymore; an AI coding agent can trigger them directly through MCP and the SDK.

Cons

• ❌ AI steps now cost 1, 3, or 5 tasks per run depending on model tier, and new steps default to the 5-task Premium tier, Zapier's most expensive, which makes costs harder to predict at real volume. 
• ❌ Zapier Agents is a separate product with its own bill, so a full agent setup can mean paying for two plans.

Lindy's Zapier alternatives roundup is a good next stop if that's the sticking point.

Best For

• A team that wants the single widest integration library and is willing to watch task consumption closely.
• A non-technical builder who wants a proven, visual automation platform for this agent.

Pricing

Zapier pricing starts with a $0/month Free plan. Professional starts at $29.99/month, Team at $103.50/month, and Enterprise is custom.

Both paid prices reflect Zapier's task-volume slider set to each plan's lowest mark: 750 tasks a month for Professional, 2,000 for Team. Moving that slider higher raises the price on every paid tier.

The separate Zapier Agents product bills on its own schedule. Free covers 400 activities a month. Pro runs $50/month for 1,500 activities. Enterprise is a custom quote.

How to evaluate Flowise alternatives

Five questions worth running through before you commit to any of these:

• How much orchestration control do you want to keep? Langflow and LangGraph hand you raw pieces to assemble; Lindy decides more for you. Picking the wrong option for your team's skills is a common reason a tool gets replaced within months.
• Does the agent need to run in more than one channel? Handing off cleanly from a website widget into Slack, memory intact hours or a day later, differs from running either channel alone. Test that handoff before committing; testing each separately won't show the gap.
• Who on your team needs a no-code AI agent builder they can safely edit? If the answer is someone non-technical, weigh that heavily. A canvas only an engineer can safely touch becomes a bottleneck the moment that engineer's busy elsewhere.
• What does the pricing model make expensive, and what does it make cheap? Credit-based tools can surprise you with cost spikes at scale. Execution-based tools get expensive fast on self-hosted infrastructure you already pay for. Flat per-seat pricing is predictable but hard to make cheap for a large team. Match the model to your growth. A demo won't show you that.
• Can you get a straight answer on cost before you build anything real? Voiceflow shows prices only after signup; Relevance AI keeps its self-serve prices in its docs, not its main pricing page. Budgeting around either is possible, but you'll need to sign up or dig into the docs first.

Looking for more than just a Flowise replacement? A wider ranked roundup of AI agent builders covers more of the category than the 10 tools reviewed here, and if a customer-facing chatbot platform is specifically what you need, Lindy's Botpress alternatives roundup narrows that down further.

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Stop treating every Flowise alternative like the same tool

Which of these Flowise alternatives fits your team comes down to one question: Do you want to keep wiring the logic together yourself, or hand that job to an agent someone without an engineering background can run and edit?

Langflow and LangGraph keep you in the driver's seat. Dify, n8n, Make, and Zapier sit in the middle: real control, but with more structure around it. Voiceflow and StackAI have compliance behind them (Voiceflow for customer-facing conversation and StackAI for enterprise RAG).

Relevance AI leads with a library of multi-agent templates to build from. Lindy is built for the moment you're done wiring things together and want the agent to work.

If that last description sounds like where you are, Lindy is worth trying against the same brief you'd run anywhere else on this list.

Frequently asked questions

Which is better for my project, Langflow or Flowise?

Langflow is the safer pick, since Flowise's own team has stopped active development.

Is Flowise completely free?

The software is free, but running it isn't. You still pay for model usage and vector storage wherever you host it. A fuller breakdown of what each of Flowise's pricing tiers included before the wind-down covers the specifics.

What is the best agentic AI currently?

The best agentic AI depends on who's building it. LangGraph fits an engineer who wants full code-level control, while Langflow suits someone who'd rather use a visual canvas than write code.

Dify fits a team that wants governance and observability built in, and Lindy fits a non-technical operator who wants a finished assistant across text and Slack.

What is Flowise AI used for?

Flowise is used to visually build AI agents and LLM-powered workflows (chatbots, document-grounded assistants, multi-step agent logic) by dragging and connecting nodes on a canvas instead of writing code.

Is n8n open source?

No, not by the standard definition. n8n's own license, the Sustainable Use License, limits use to internal business or non-commercial purposes.

That makes n8n source-available, not open source: it allows reading and modifying the code, but not the free redistribution and resale the OSI's definition requires.

What's the difference between Flowise and Dify?

Flowise gives you three builders to assemble yourself, while Dify comes opinionated, with governance built in.