LangFlow vs Flowise: Which AI Workflow Builder Should You Use?

Visual workflow builders for LLM applications let you design agent systems without writing orchestration code from scratch. LangFlow and Flowise both provide node-based interfaces for building AI workflows, but they differ in architecture, deployment approach, and target users.

LangFlow is Python based with source code access for every component. Flowise runs on Node.js with a focus on ease of use and quick deployment. Both are open source and support production workloads. 

So, your choice depends on your stack, technical requirements, and whether you prioritize flexibility or speed.

Let's compare both!

What Are LangFlow and Flowise?

LangFlow is an open-source Python framework for building AI applications with a visual editor. It supports agents, the Model Context Protocol (MCP), and works with any LLM or vector store, avoiding vendor lock-in. You can author workflows visually, or run them via API/MCP servers to create reusable tools.

Key components include:

• Language models, vector databases, agents, tools, and data processors.

• Full customization through Python source code.

• An interactive playground for step-by-step testing with immediate feedback.

LangFlow handles multi-agent orchestration, conversation management, and retrieval workflows. Observability is integrated through LangSmith and LangFuse. Workflows can be deployed as APIs, exported to JSON for Python apps, or deployed as MCP servers, enabling flows to act as tools for MCP clients.

It is maintained by DataStax as open source. Installation requires Python 3.10–3.13 and is supported via pip/uv or Docker. LangFlow Desktop offers a standalone application for Windows and macOS with all dependencies included.

Flowise: Overview

Flowise is an open-source generative AI platform built on Node.js for creating AI agents and LLM workflows. It provides three visual builders:

• Assistant: Beginner-friendly creation of chat assistants with instructions, tools, and RAG capabilities using uploaded files.

• Chatflow: Single-agent systems and chatbots with advanced techniques like Graph RAG, rerankers, and retrievers.

• Agentflow: Multi-agent orchestration for complex workflows.

The platform integrates with over 100 data sources, tools, and vector databases. Additional features include execution logs, visual debugging, input moderation, output post-processing, and customizable embedded chat widgets. For enterprise setups, it provides RBAC, SSO, encrypted credentials, rate limiting, and domain restrictions.

FlowiseAI maintains the project under an Apache 2.0 license. It requires Node.js >= 18.15.0 and runs through npm or Docker. The platform also offers API access, JavaScript and Python SDKs, CLI tools, a template marketplace, and evaluation tools with datasets and evaluators.

Key Features Compared

User Interface & Ease of Use

LangFlow uses a visual editor where you drag components onto a canvas and connect them. Each component exposes configurable parameters, and the playground allows testing components individually for isolated debugging. You can access and modify the Python source code of any component, giving full transparency and control, though this requires Python knowledge. The interface assumes familiarity with AI concepts such as embeddings, retrievers, and agent patterns.

Flowise provides three interfaces for different skill levels:

• Assistant: Guided setup for simple chat assistants.

• Chatflow: More flexibility with additional component options.

• Agentflow: Complex orchestration with branching, looping, and routing.

Flowise includes a template marketplace for ready-to-use workflows. Testing is done through built-in chat interfaces, and visual debugging displays execution logs to help identify issues. The platform is easier for non-developers to pick up, but still assumes basic AI understanding.

Integration & Ecosystem

LangFlow connects to major LLM providers (OpenAI, Anthropic, Cohere, HuggingFace), vector databases (Pinecone, Weaviate, Qdrant, Chroma), and AI tools. Custom Python expressions allow fine-tuned component behavior. 

Its MCP integration lets flows act as both client and server, enabling use in MCP-compatible applications. Authentication, environment variables, and encrypted credentials support secure deployments.

Flowise supports over 100 integrations, including vector databases, memories, tools, and data sources. 

Custom integrations use JavaScript or TypeScript. Components handle data transforms, filters, aggregates, and RAG indexing. Enterprise features include RBAC, secret manager support, and secure credential handling.

Scalability & Performance

LangFlow workflows can be exported to JSON for Python apps and run via API or embedded in custom applications. It supports vertical and horizontal scaling for high workflow loads. Deployment options include self-hosting, Docker, and major cloud platforms. LangFlow Desktop simplifies local development with all dependencies included.

Security options cover enterprise needs, including air-gapped environments.

Flowise exposes workflows as REST APIs with built-in servers. Docker export enables deployment on platforms like AWS, Azure, GCP, and Digital Ocean. The platform handles request queuing, rate limiting, and response streaming. Managed hosting via Flowise Cloud provides teams, workspaces, tracing, analytics, and evaluation tools. Self-hosted deployments allow horizontal scaling, while performance mainly depends on LLM API latency.

Template, Marketplace & Community Support

LangFlow offers pre-built templates for common patterns, with community contributions adding components and shared workflows. Documentation covers installation, component development, and deployment. 

Observability integrations with LangSmith and LangFuse support production monitoring. GitHub activity shows regular updates and active development.

Flowise provides a template marketplace with workflows for chatbots, document analysis, content generation, and data extraction. Templates are guided, making them accessible to business users. 

Its three builders support progressive complexity, from simple assistants to advanced multi-agent systems. Community support happens through Discord and GitHub, and documentation includes step-by-step guides and video tutorials.

Use Cases and Applications

Prototyping and Experiments

LangFlow lets you design workflows visually and test components step by step. Python access allows modifying parts of the workflow during experimentation, and prototypes can be exported to production-ready code.

• Explore agent architectures and LLM combinations.

• Validate workflow logic visually.

• Transition prototypes to Python applications easily.

Flowise allows fast creation of prototypes. Assistant mode lets you set up chat assistants with uploaded documents and instructions. Chatflow adds options for custom retrieval strategies, and templates provide ready-made starting points.

• Rapid MVP or internal tool development.

• Quick testing through chat interfaces.

• Built-in deployment supports early user testing.

Production-Ready Workflows & Multi-Agent Systems

LangFlow supports API deployment and Python integration. MCP server deployment lets workflows act as tools in agent frameworks. Multi-agent orchestration and custom components integrate with existing pipelines, with monitoring via LangSmith and LangFuse.

• Deploy workflows as APIs or Python code.

• Multi-agent coordination with custom behaviors.

• Production monitoring and logging.

Flowise provides production features including RBAC, rate limiting, and SSO. Agentflow handles multi-agent orchestration, while logs and evaluation tools support quality assurance. Embedded chatbots and APIs allow integration with applications.

• Secure multi-agent workflows.

• Execution logs and human-in-the-loop evaluation.

• Integration through APIs or embedded chat.

Strengths and Weaknesses

LangFlow: Pros & Cons:

LangFlow gives developers full control and flexibility. It’s strong for Python workflows and custom pipelines, but it assumes technical experience and some setup effort.

Pros	Cons
Fits Python/data science workflows	Requires Python knowledge
Full source code access for customization	Steeper learning curve
MCP server deployment enables reusable tools	Less focus on templates
Desktop app includes dependencies	Deployment may need extra setup
Observability via LangSmith/LangFuse	Documentation assumes technical background

Flowise: Pros & Cons:

Flowise is easier to start with, with templates and multiple builders. It works well for teams using JavaScript/Node.js and for rapid prototypes, but it’s less flexible for deep customization.

Pros	Cons
Three builders support different skill levels	Node.js may not fit Python teams
Template marketplace speeds development	Less flexible than direct code
Built-in evaluation tools	Customization requires JavaScript/TypeScript
Enterprise features: RBAC, SSO, rate limiting	Opinionated architecture patterns
Embedded chatbot for web integration	Evaluation features can be overkill for simple cases

When LangFlow Makes the Most Sense

LangFlow is best for Python-focused teams or projects that need full control and deep customization. Its source code access and Python foundation make it easy to integrate with data pipelines, ML workflows, and existing Python apps. MCP server support allows workflows to function as tools in larger agent systems, and observability integrations help monitor production deployments.

• Python-based teams and developers.

• Projects requiring custom components or advanced workflows.

• MCP server workflows and multi-agent systems.

• Local development with the Desktop app or air-gapped deployments.

When Flowise Is the Better Fit

Flowise is designed for projects where rapid development and ease of use are priorities. Assistant mode lets non-developers create functional agents quickly, and templates provide tested starting points. Enterprise features like RBAC, SSO, and evaluation tools support secure deployments. Teams, workspaces, and managed hosting simplify collaboration and infrastructure management.

• Rapid prototypes or MVPs.

• Teams with limited programming experience.

• Enterprise security and quality assurance requirements.

• Collaborative development with managed hosting or Flowise Cloud.

Your Next Move

LangFlow and Flowise provide visual AI workflow builders for different needs. LangFlow focuses on Python integration and full customization, while Flowise offers accessibility with three builder modes and templates.

• Python teams or custom agents: LangFlow.

• Fast development, enterprise features, or JavaScript: Flowise.

• Production workloads: LangFlow provides Python code, Flowise handles managed infrastructure.

The easiest way to compare them is to try a small workflow in each. LangFlow Desktop and Flowise’s npm install keep the setup light.

If you need help selecting or setting up the right workflow builder, reach out to us to get it running efficiently.

Frequently Asked Questions

What is the difference between LangFlow and Flowise?

LangFlow is a Python-based visual framework with source code access for every component and MCP server capabilities. Flowise is a Node.js platform offering three builder interfaces (Assistant, Chatflow, Agentflow) with enterprise features like RBAC, evaluations, and template marketplace. LangFlow targets Python developers needing customization. Flowise supports multiple skill levels, from beginners to advanced users.

Is Flowise better than LangFlow?

Neither is universally better. Flowise works better for Node.js teams, organizations needing enterprise security features, and users wanting guided experiences through templates. LangFlow works better for Python developers, teams requiring deep customization, and projects needing MCP integration. Choose based on your stack and technical requirements.

Can I use LangFlow or Flowise in production?

Yes, both support production deployments. LangFlow deploys as API endpoints, exports to Python applications, or runs as MCP servers with observability through LangSmith/LangFuse. Flowise includes built-in security (RBAC, SSO, rate limiting), evaluation tools, external log streaming, and managed hosting through Flowise Cloud. Both support Docker deployment and major cloud platforms.

Are LangFlow and Flowise open source?

Yes, both are open source. LangFlow is maintained by DataStax, with Python source code available on GitHub. Flowise uses Apache 2.0 license maintained by FlowiseAI with active community on GitHub and Discord. Both accept community contributions and provide self-hosting options.