7 Best Flowise Alternatives After Its 2026 Sunset
Flowise is winding down. New development stopped on July 29, 2026, its GitHub repository is scheduled to become a public archive on August 10, and official core-team support in GitHub and Discord ends August 31. The Apache-2.0 code will remain visible and can be forked, but npm packages and Docker images are being marked deprecated.
One distinction matters: Flowise has not published a date when Flowise Cloud will switch off or customer data will be deleted. Its sunset notice covers the open-source project, packages, images, and official community support. Treat any claim that “Flowise Cloud shuts down August 31” as unverified unless you received a separate customer notice.
The practical decision is still urgent. Staying on a frozen fork means owning maintenance and security patches. Moving means rebuilding because Flowise’s JSON export preserves its graph, not a portable standard that another visual builder can import perfectly.
The first decision is whether you still need a builder. If Flowise was only the machinery behind business tasks, a finished agent platform removes the canvas and its maintenance entirely. If you are shipping an AI application or need node-level control, move to another framework. The alternatives below keep that distinction explicit.
Flowise alternatives at a glance
| Alternative | Visual builder | Self-host | License | Best for |
|---|---|---|---|---|
| Carly | Plain-English setup | No | Proprietary | Finished agents across business apps |
| Langflow | Yes | Yes | MIT | Closest overall replacement |
| Dify | Yes | Yes | Modified Apache-based | RAG and complete AI apps |
| n8n | Yes | Yes | Sustainable Use License | Integration-heavy automation |
| LangGraph + LangSmith | Studio/debugging | Yes | MIT framework | Complex code-first agents |
| StackAI | Yes | Enterprise | Proprietary | Regulated enterprise workflows |
| Relevance AI | Yes | No | Proprietary | No-code multi-agent teams |
1. Carly
Carly is the strongest alternative when the goal is to automate the work, not preserve the Flowise canvas. You describe the job in plain English, limit which tools the agent may use, and run it on a schedule or when mail arrives. Agents can have their own email addresses, instructions, and memory, and work across around 260 native integrations. Any additional service with a public API can connect with your own key.
This covers the operational jobs many teams built in Flowise: qualify an inbound lead, update the CRM, prepare and send a follow-up, route a support request, reconcile an invoice, assemble a daily briefing, or coordinate calendar and project updates. Carly handles the integrations, execution, and ongoing agent behavior instead of handing your team another graph to host and maintain.
vs. Flowise: Carly is a finished agent platform, not an application framework. You give up arbitrary node-level composition and self-hosting in exchange for not operating the runtime, credentials layer, vector infrastructure, or connector code. It is not the right replacement if you are building a white-label AI product for your own customers. It is the clearest upgrade if Flowise was internal machinery for getting business work done.
Pricing: free Zapier-style workflows; AI agents from $35/month.
Best for: teams that want the outcome without owning another agent-builder stack.
2. Langflow
Langflow is the closest conceptual replacement for Flowise: an open-source, node-based builder for agents and LLM applications, with Python underneath when the canvas stops being enough. It supports custom components, model-agnostic flows, a playground, multi-agent orchestration, API deployment, MCP-server deployment, and JSON export.
The project is MIT licensed. You can run it as a desktop app, Python package, Docker container, or cloud deployment. Langflow also offers a hosted cloud account, although its public site does not currently expose a clean quota-and-price table to compare against self-hosting.
vs. Flowise: the mental model transfers best here. Both use a visual graph and LangChain-adjacent components, but Langflow’s Python foundation is the more natural escape hatch for custom behavior. You will still rebuild and test each production flow; Flowise JSON is not a one-click Langflow import.
Best for: teams that want the nearest open-source visual replacement.
3. Dify
Dify is broader than a flow canvas. It combines visual workflows, RAG ingestion and retrieval, prompt management, agents, model routing, generated APIs, application interfaces, and operational logs in one platform.
The Sandbox cloud tier is free for one user, five apps, 50 documents, and 5,000 API calls per month. Professional is $590 per workspace per year; Team is $1,590 per workspace per year. You can also deploy the community edition with Docker or buy private and enterprise deployment.
vs. Flowise: Dify is stronger when the flow is only one part of a shipped AI product. Knowledge pipelines and app operations are built in instead of assembled around the canvas. The tradeoff is licensing: Dify’s source license adds conditions to Apache 2.0, including restrictions around multi-tenant services and removing its frontend branding.
Best for: teams building RAG apps, chat products, and internal AI tools end to end.
4. n8n
n8n is the best move when the Flowise graph spends most of its time calling other systems. It has a deep connector catalog, triggers, schedules, branching, retries, credential management, and AI nodes, so an LLM can sit inside an operational workflow instead of being the whole product.
Starter Cloud is €20/month billed annually for 2,500 executions. Pro starts at €50/month. The self-hosted Community Edition is free, while advanced self-hosted governance is sold on the €667/month Business plan or Enterprise. Paid plans meter completed workflow executions rather than every node.
vs. Flowise: n8n is more mature at moving data through SaaS tools and less specialized at RAG, agent memory, and conversational applications. Its code is source available under the Sustainable Use License, not OSI open source, so read the limits before embedding or reselling it. Our full n8n alternatives comparison covers adjacent automation platforms.
Best for: AI workflows that must trigger and take action across business systems.
5. LangGraph with LangSmith Studio
LangGraph is a code-first framework for stateful agents. It gives engineers explicit control over state, durable execution, memory, resumability, human approval, and long-running loops. LangSmith adds tracing, evaluation, deployment, and Studio for inspecting and debugging the graph.
The LangGraph framework is free under MIT. LangSmith Developer is free for one seat and 5,000 base traces per month but excludes hosted deployment. Plus costs $39 per seat per month plus usage and includes one small serverless deployment.
vs. Flowise: this is not a replacement canvas. Studio visualizes and debugs a graph you wrote in code; it does not turn a non-developer into a drag-and-drop author. The reward is far more control when visual nodes become a liability—the exact complexity ceiling Flowise cited in explaining its sunset.
Best for: engineering teams building production agents whose state and failure modes must be explicit.
6. StackAI
StackAI is a proprietary visual platform aimed at enterprise knowledge and document workflows. It combines loaders, document readers, knowledge bases, multimodal inputs, models, agents, and API endpoints, with SSO, access controls, compliance support, and private infrastructure on Enterprise.
The free tier covers one user, two projects, and 500 runs per month. Everything beyond that moves to custom Enterprise pricing; there is no published self-serve middle tier.
vs. Flowise: StackAI trades open-source control for a supported enterprise product and private deployment. It is compelling when procurement, identity, and data location dominate the decision, and much less compelling if you chose Flowise specifically for its permissive license and low-cost self-hosting.
Best for: regulated companies deploying document and knowledge agents privately.
7. Relevance AI
Relevance AI packages agents as a no-code “workforce.” Teams create specialized agents, give them tools and integrations, connect them into multi-agent processes, and add escalations or human approval. The platform advertises more than 2,000 integrations and includes an agent marketplace.
Free includes 200 actions per month. Pro is $19/month billed annually for 2,500 actions, two builders, unlimited workforces, and bring-your-own LLM. Team is $234/month annually for 7,000 actions, five builders, and 45 end users. Every tool run counts as an action even when it fails, while model usage draws from a separate vendor-credit balance.
vs. Flowise: Relevance AI is easier for business teams and more opinionated around fleets of workers. It is a hosted proprietary service, with self-hosting unavailable and single-tenant deployment still in development. Region choice is also sticky, so decide where the organization should live before loading data.
Best for: non-developer teams coordinating multiple role-specific agents.
How to migrate off Flowise safely
Export is the beginning, not the migration. Before the old environment changes, capture:
- Every chatflow and agentflow as JSON. Preserve production flow IDs and current endpoints as a reference.
- Credentials separately. Flowise exports do not include credentials. Inventory API keys, OAuth connections, webhooks, and the nodes that use each one.
- Self-hosted encryption material. If you are moving the existing instance, retain the credential database or store and the matching encryption key. Without both, encrypted credentials are not recoverable.
- External state. Record vector databases, document stores, uploaded files, custom nodes, environment variables, queues, and callback URLs.
- Behavioral tests. Save representative inputs and expected outputs for every important path, including model failures and tool timeouts.
- Credential rotation. Recreate connections on the replacement, test them, then rotate secrets and retire the old endpoints.
There is no official converter from Flowise into another platform. Expect the JSON to help you understand the old graph, not import it automatically.
Which Flowise alternative should you choose?
Choose Carly when Flowise was internal machinery and you want the business task handled without maintaining another builder. Choose Langflow for the closest visual open-source replacement. Choose Dify when you need RAG, app interfaces, logs, and deployment in one product. Choose n8n when you still want a workflow canvas and integrations matter more than the LLM graph.
Choose LangGraph when a codebase with explicit state is preferable to another canvas. Choose StackAI for vendor-backed private enterprise deployments. Choose Relevance AI when business users specifically need to assemble and coordinate multiple agents in a hosted product.
Forking Flowise remains a legal option under Apache 2.0. It is not a neutral option: once upstream development, official support, and published packages stop, your team becomes the maintainer. For a production system, compare the cost of that responsibility against migration now.
FAQ
Is Flowise shutting down?
Flowise says it is winding down operations. New development stopped July 29, 2026, the repository is scheduled for archive status August 10, and official core-team community support ends August 31. The code remains available under Apache 2.0. Flowise has not publicly specified a Flowise Cloud shutdown or customer-data deletion date.
Can I keep self-hosting Flowise?
Yes. The source remains available under Apache 2.0 and Flowise explicitly encourages forks. You will be responsible for maintenance, dependency updates, security patches, packaging, and compatibility after the official project freezes.
What is the closest Flowise alternative?
Langflow is the closest overall because it combines an open-source visual graph, model-agnostic components, Python customization, APIs, and self-hosting. Dify is a better choice when you want a complete RAG and AI application platform around the workflow.
Does Flowise JSON import into Langflow or Dify?
Not directly. Flowise can export graphs and other selected data as JSON, but competing builders use different node types, credentials, runtime assumptions, and schemas. Use the export as a migration reference and rebuild each production flow with tests.
Are Flowise credentials included in exports?
No. Flowise’s migration documentation says credentials are not exported and must be recreated and reconnected. For a self-hosted instance, encrypted credentials also depend on the matching encryption key.
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