If you are choosing a visual AI-agent builder today, do not evaluate these three products as if they occupy the same current state. Flowise reached end of life in August 2026. That leaves Dify and Langflow as the maintained choices for new projects, with Flowise relevant mainly to teams that already run it or deliberately maintain a fork.
The short answer
Choose Dify when you want a more integrated AI application platform: visual workflows, first-class knowledge/RAG pipelines, agents, model and tool integrations, publishing, monitoring, and managed or self-hosted deployment choices.
Choose Langflow when you want a component-oriented visual canvas with an easy path into Python, built-in API and MCP delivery, permissive MIT licensing, and an actively maintained upstream project.
Keep or fork Flowise only when you already depend on it and your team is prepared to own maintenance. Its Apache 2.0 source remains available, but the official project is archived and no longer provides an active upstream maintenance path.
Dify vs Langflow vs Flowise at a glance
| Area | Dify | Langflow | Flowise |
|---|---|---|---|
| Current status | Active | Active | End of life / archived |
| Primary orientation | AI application, RAG and agent platform | Visual component graph for agents and workflows | Legacy visual agent/workflow builder |
| Knowledge/RAG | First-class Knowledge Pipeline | Composable through components, vector stores and retrieval | Historical RAG workflows; no active upstream |
| Code extensibility | Plugins, tools and integrations | Custom Python components | Fork/custom code possible; upstream archived |
| API/MCP | Apps can be published as APIs and MCP-compatible tools | Built-in API and MCP servers | Legacy integrations only |
| License | Apache-2.0-derived Community license | MIT | Apache 2.0 source archive |
| Self-hosting | Community and Enterprise options | Python, Docker and cloud deployment | Possible from archived source |
| Best fit | Teams operating AI apps and RAG systems | Developers wanting visual iteration plus code control | Existing deployments or maintained forks |
Dify: best for an integrated AI application platform
Dify is the broadest platform of the three in 2026. Its current product combines visual Workflow Studio, agent building, a dedicated Knowledge Pipeline, model and tool integrations, application publishing and monitoring.
The first-class Knowledge Pipeline is a major differentiator. Dify can ingest files, websites, online documents and drives, then extract, clean, chunk, index and test retrieval before the knowledge is attached to an application. That makes RAG a product surface rather than a collection of nodes a team has to organize independently.
Dify also offers multiple deployment paths. Dify Cloud reduces infrastructure work, Community Edition can be self-hosted, and Enterprise adds governance features such as SSO/SAML, RBAC and audit logs for private or VPC deployments.
The main trade-off is platform weight. A team that only wants to sketch an agent graph may not need Dify’s application, knowledge and operations layers. Its Community license also deserves careful reading because Dify describes it as Apache-2.0-derived rather than plain Apache License 2.0.
Langflow: best for visual composition with Python escape hatches
Langflow takes a more component-oriented approach. Its visual builder makes it easy to connect models, prompts, retrieval, tools and agents, but developers can customize components directly in Python when the canvas is not enough.
That hybrid matters for engineering teams. A visual graph remains useful for communicating system structure, but unusual validation, transformation or integration logic can live in code instead of being forced into increasingly awkward nodes.
Langflow also makes flows reusable outside the editor. Its current project documentation describes built-in APIs and MCP servers, so a visual flow can become an endpoint for an application or a tool for compatible MCP clients.
Maintenance is another advantage. As of October 8, 2026, Langflow’s GitHub repository is not archived and includes release-1.12.5 activity from October 6. In a fast-moving agent ecosystem, that active upstream reduces the amount of compatibility work teams must carry privately.
The MIT license is also simpler than Dify’s Community licensing for organizations that prioritize permissive open-source terms.
Flowise: legacy and fork option, not a new default
Flowise changed category during 2026. It is no longer an active third competitor for new deployments.
Flowise’s official sunset notice set a July 29 code freeze, August repository archival and August 31 end of life. Its GitHub repository is now archived.
The source code remains available under Apache License 2.0, and the Flowise team explicitly encouraged forks. That gives existing users a legitimate path to keep running a stable deployment or maintain their own branch.
The official Flowise upstream is archived. Teams that keep Flowise therefore need an explicit maintenance owner for their fork, including provider API changes, package vulnerabilities, MCP compatibility and container updates.
For a new project, there is little reason to accept that burden when Dify and Langflow are actively maintained.
RAG and knowledge management
Dify has the clearer advantage when RAG is central to the application. Its Knowledge Pipeline turns ingestion, cleaning, chunking, indexing and retrieval testing into a dedicated workflow. For internal knowledge assistants, support bots or document-heavy applications, that structure can reduce custom glue work.
Langflow can build strong RAG pipelines too, but it treats retrieval as a composable system. Developers choose vector databases, retrieval components, model steps and custom Python logic as part of the visual graph. That is often preferable when the team already has its own data architecture or wants to experiment with retrieval strategies.
The distinction is not that Dify can do RAG and Langflow cannot. Dify productizes more of the knowledge lifecycle, while Langflow gives developers a more open component canvas.
Flowise historically supported RAG workflows and was popular for fast chatbot prototypes, but new RAG projects should not start from an archived upstream unless a team is consciously adopting a maintained fork.
Extensibility: plugins, Python, tools and MCP
Dify’s extensibility centers on its Marketplace and plugin model. Teams can install model providers, tools, data sources and MCP integrations or define their own plugins. This works well when a team wants a managed catalog of extensions inside a larger application platform.
Langflow’s strongest escape hatch is Python. Developers can customize components directly and combine that code with visual orchestration. Its built-in API and MCP servers also make the resulting flows easy to expose as reusable interfaces.
For teams with experienced Python developers, Langflow’s model can feel more natural because the boundary between graph and code is explicit. For teams that want more of the application lifecycle handled inside one product, Dify’s plugin and platform structure is often easier to operationalize.
In both cases, external tools should be treated as production dependencies. Plugins, MCP servers and custom components can receive data or perform actions, so installation convenience should not replace permission review.
Deployment, licensing and operational control
Dify and Langflow both support self-hosted deployment, but the licensing and operating models differ.
Dify’s Community Edition is self-hostable, while Enterprise targets private and VPC deployments with governance controls. Its current site describes the Community license as Apache-2.0-derived with additional conditions. Organizations embedding Dify into a commercial product or multi-tenant service should review those terms directly.
Langflow is MIT-licensed and can run locally with Python, in Docker or on major cloud environments. That permissive license is straightforward for teams that expect to modify, redistribute or deeply integrate the software.
Flowise’s archived source remains Apache 2.0. The license is permissive, but the operational cost is ownership of an unmaintained upstream.
Self-hosting any of these systems does not make the surrounding stack free. Model APIs, vector databases, storage, observability, secrets management, upgrades and engineering time still create cost.
Migration guidance for existing Flowise users
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If you already run Flowise, migration should start by inventorying the flows, model providers, vector stores, credentials, custom nodes and external integrations you actually use. Separate portable application logic from Flowise-specific implementation details before rebuilding anything.
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Move first to Langflow when your current setup depends on a node-and-component graph and your developers want to preserve a visual workflow while moving custom logic into Python. Move to Dify when the larger goal is to consolidate RAG ingestion, agent workflows, publishing and operational management inside one application platform.
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Do not migrate by recreating every Flowise node one-for-one. Re-evaluate the architecture instead: map each existing workflow to its business purpose, identify obsolete dependencies, rotate secrets during the move, and run side-by-side validation on representative prompts before cutting traffic over. Keep the old Flowise instance available only for a defined rollback window, then retire it once the replacement passes functional and security checks.
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Maintenance and production risk
Maintenance status is the biggest reason this comparison changed in 2026. Dify is active and continues to evolve its workflow, knowledge and application platform. Langflow is active and was shipping release work in October 2026. Flowise is archived.
That difference matters for agent builders because these tools sit on top of model SDKs, vector databases, MCP tooling, API providers and fast-moving dependencies. An active upstream can absorb compatibility and security work that would otherwise become a private maintenance burden.
An existing Flowise installation can still be defensible, but a new Flowise deployment needs a stronger justification than interface preference. The team should know who owns the fork and how it will be patched.
Which builder fits which team?
| Scenario | Best fit | Why |
|---|---|---|
| Internal RAG assistant with managed knowledge ingestion | Dify | First-class Knowledge Pipeline and broader app lifecycle |
| Visual agent prototyping with custom Python logic | Langflow | Component graph plus Python customization |
| Reusable agent tool exposed through MCP | Langflow or Dify | Both support MCP-oriented delivery with different platform models |
| Enterprise private deployment | Dify Enterprise | Private enterprise deployment and governance features |
| Permissive OSS integration into a developer stack | Langflow | MIT license and active upstream |
| Existing Flowise environment that cannot migrate immediately | Flowise fork/legacy | Apache 2.0 source remains available, but maintenance must be owned |
Privacy, safety and responsible use
Self-hosting improves control over the application layer, but it does not automatically keep all data private. If a Dify or Langflow workflow calls an external model, vector database or MCP service, the data policies of those services still matter.
Protect model keys and database credentials as production secrets. Apply least privilege to tools that can modify business systems. Review third-party plugins, Python components and MCP servers before production use.
RAG systems should enforce document-level access control so retrieval does not expose information a user is not authorized to see. Agent actions that are irreversible or financially meaningful should normally retain human review or another explicit control.
For legacy Flowise deployments, add upstream maintenance to the risk register. An archived project should not remain internet-facing indefinitely without a defined patching and dependency strategy.
Final verdict
For new production deployments in October 2026, this is effectively a two-way decision.
Choose Dify if your priority is operating an AI application: RAG, agents, publishing, monitoring, managed or private deployment and a more integrated lifecycle.
Choose Langflow if your priority is composing agent logic visually while retaining Python control, permissive MIT licensing and direct API/MCP delivery.
Do not choose Flowise as a new default. Its code is still valuable and existing installations can be maintained through forks, but the official upstream has reached end of life.
The better tool is determined by architecture rather than the number of nodes on a canvas: Dify for the application platform, Langflow for the developer-oriented component graph, and Flowise only when legacy continuity outweighs the cost of owning maintenance.
Frequently asked questions
Is Flowise still worth using in 2026?
Only for existing deployments or forks with an explicit maintenance owner. Flowise reached end of life on August 31, 2026 and its official repository is archived.
Dify vs Langflow: which is better for RAG?
Dify is usually better when you want a first-class managed knowledge pipeline. Langflow is stronger when you want to compose and customize retrieval components inside a visual graph.
Which has the most permissive license?
Langflow uses the MIT license. Flowise’s archived source uses Apache License 2.0. Dify’s Community Edition uses an Apache-2.0-derived license with additional conditions.
Can Dify and Langflow both be self-hosted?
Yes. Dify offers self-hosted Community and Enterprise paths. Langflow can run locally, in Docker or on major cloud infrastructure.
What should Flowise users migrate to?
Choose Langflow if you want to preserve a visual component-graph workflow with code extensibility. Choose Dify if you want a broader RAG and AI application platform around the workflow builder.
