Flowise vs Langflow: Open-Source Agent Builder Comparison

Flowise and Langflow occupy the same niche: open-source visual builders that let you construct LLM-powered AI agents and RAG pipelines by connecting nodes on a canvas rather than writing application code. Both emerged in 2023 as the visual abstraction layer over LangChain that the developer community wanted, and both have matured into production-capable platforms.

For teams that have evaluated code-first frameworks but want faster iteration cycles, a lower learning barrier, and visual debuggability, Flowise and Langflow are the two most established options in the open-source space.

For broader context on visual builders vs code-first frameworks, see Dify vs LangChain and Open-Source vs Commercial AI Agent Frameworks.

Decision Snapshot#

• Pick Flowise when your team is comfortable with JavaScript/Node.js, you want the fastest path to a working LangChain visual workflow, and community template availability is a priority.
• Pick Langflow when your team works in Python, you need a richer component library with non-LangChain integrations, or API-first deployment with a polished REST interface matters.
• Consider Dify as an alternative if your primary users are non-technical — both Flowise and Langflow still expect technical literacy from builders.

Feature Matrix#

| Dimension | Flowise | Langflow | |---|---|---| | Language stack | JavaScript / TypeScript (Node.js) | Python | | Built on | LangChain.js | LangChain Python + custom components | | Visual canvas | Yes — node-graph builder | Yes — node-graph builder | | LLM support | OpenAI, Anthropic, Azure OpenAI, Ollama, and more | OpenAI, Anthropic, Google, Ollama, and more | | Vector store support | Pinecone, Chroma, Weaviate, Qdrant, pgvector, and more | Pinecone, Chroma, Weaviate, Qdrant, pgvector, and more | | Agent support | Tool-using agents, ReAct, OpenAI Functions | Agents, CrewAI integration, tool binding | | Multi-agent | Basic multi-agent chains | CrewAI integration for role-based collaboration | | RAG pipeline support | Full RAG — document loaders, splitters, embeddings | Full RAG — document loaders, splitters, embeddings | | Custom components | Custom nodes via JavaScript | Custom components via Python | | API deployment | REST API endpoint per flow | REST API endpoint per flow (Langflow API) | | Self-hosting | Docker, npm, cloud VMs | Docker, pip, cloud VMs | | Cloud-hosted option | Flowise Cloud (paid) | Langflow Cloud (paid, backed by DataStax) | | License | MIT | MIT | | Community size | Large, active Discord + GitHub | Growing, significant VC backing | | Template library | Large community template collection | Growing marketplace | | UI polish | Functional, community-oriented | More polished, enterprise-oriented |

What Flowise Is#

Flowise is an open-source visual tool for building LLM applications by chaining LangChain.js components on a drag-and-drop canvas. It was created by a small team and released publicly in early 2023, quickly gaining traction with the developer community for its accessibility relative to raw LangChain code.

Flowise's primary advantage is its Node.js stack and close alignment with LangChain.js. For teams already working in the JavaScript ecosystem, the extension model is natural — custom nodes are JavaScript modules. This makes it particularly accessible to full-stack web developers who are new to AI but familiar with Node.js.

The Flowise interface centers on a flow canvas where you drag component nodes and connect them. Common patterns — retrieval QA chains, conversational agents, tool-using agents — have pre-built node sequences. A chatbot backed by a PDF knowledge base can be built and tested in 20-30 minutes without code.

Flowise ships with a built-in chatbot widget that can be embedded in any website, plus a REST API for calling flows programmatically from other applications. This makes it practical not only as a prototyping tool but as a backend service layer for simple AI products.

Key Flowise characteristics:

• Built on LangChain.js — all LangChain.js abstractions available as visual nodes
• Large library of community-contributed template flows
• Built-in embed widget for chatbot deployment
• REST API for programmatic flow invocation
• Active community on GitHub (19,000+ stars as of 2026) and Discord
• Flowise Cloud for managed hosting; self-host for free

For implementation guidance with LangChain patterns, see Build AI Agents with LangChain.

What Langflow Is#

Langflow is an open-source visual development environment for building AI agents and RAG applications, originally built as a visual interface for LangChain in Python. In 2024, Langflow raised significant venture funding and was acquired by DataStax, giving it a larger engineering team and longer-term investment horizon than Flowise.

Langflow's Python stack aligns with how most AI and data engineering teams work. Custom components are Python classes, making extension natural for teams already writing Python data pipelines, ML models, or AI services.

Beyond LangChain, Langflow has expanded to include non-LangChain components: OpenAI's Assistants API, CrewAI for multi-agent workflows, and direct model API integrations. This makes it less LangChain-specific and more of a general-purpose visual AI workflow builder — a positioning shift that increases component library breadth but also increases platform complexity.

Langflow's API layer is polished and designed for production use. Each deployed flow gets a stable REST endpoint with schema validation and token-based authentication. DataStax's backing means the cloud-hosted Langflow offering is better resourced than Flowise Cloud and includes AstraDB vector store integration out of the box.

Key Langflow characteristics:

• Built on Python — extends with Python classes
• CrewAI integration for multi-agent role-based collaboration
• Polished REST API deployment model with authentication
• DataStax (AstraDB) integration for vector storage
• Cloud hosting backed by DataStax infrastructure
• Growing enterprise positioning alongside community adoption
• Broader component library beyond pure LangChain

For multi-agent architecture context, see Build Multi-Agent Systems with CrewAI and Multi-Agent Systems Guide.

Deep Dive: RAG Pipeline Building#

Both platforms handle standard RAG pipeline construction well. The canonical pattern — ingest documents, chunk text, create embeddings, store in a vector database, retrieve on query, generate answer — is achievable in both with no code.

In Flowise, a RAG flow typically involves: a Document Loader node (PDF, URL, or text), a Text Splitter node, an Embeddings node (OpenAI or local model), a Vector Store node (Chroma, Pinecone, or others), and a Retrieval QA Chain node that ties it together with an LLM. The flow runs end-to-end from the canvas.

In Langflow, the same pattern uses equivalent component types with Python-backed processing. Langflow's AstraDB integration (from DataStax) means teams using Langflow Cloud get a managed vector store included, reducing the setup overhead for the storage layer.

For teams evaluating RAG quality, neither platform fundamentally changes the performance ceiling — retrieval quality is determined by chunking strategy, embedding model, and query design, not the visual builder wrapper. See Introduction to RAG for AI Agents for RAG architecture depth.

Deep Dive: Extending with Custom Components#

Both platforms support custom component development, but the experience differs.

In Flowise, custom nodes are JavaScript/TypeScript modules that implement a specific interface. If you are comfortable with Node.js development, the extension model is relatively accessible. The Flowise documentation covers custom node development with examples.

In Langflow, custom components are Python classes decorated with Langflow's component API. Python developers familiar with class-based abstractions will find the model intuitive. The Python alignment also means you can import existing Python AI libraries (Hugging Face Transformers, Sentence Transformers, custom LLM clients) directly into custom components.

The practical implication: if your team's engineering is primarily Python-based, Langflow's extension model will feel more natural. If your team works in JavaScript/TypeScript or is primarily web development oriented, Flowise's extension model is a better fit.

Use-Case Recommendations#

Choose Flowise when:#

• Your team's primary language is JavaScript/TypeScript or you are a full-stack web developer
• You need a fast path to a working visual LangChain workflow with maximum community template availability
• You want an embeddable chat widget for quick deployment to a web application
• Self-hosted open-source without commercial cloud dependency is a priority

Choose Langflow when:#

• Your team works in Python and extending the platform with custom Python components is anticipated
• You need CrewAI multi-agent integration within the visual builder
• A polished API deployment model with authentication and schema validation is important
• You want cloud hosting backed by an enterprise-grade provider (DataStax)

Consider both for evaluation if:#

• Your team is evaluating the visual builder category before committing
• You want to prototype in both and compare RAG quality and developer experience for your specific use case

Verdict Summary#

Flowise and Langflow are closely matched platforms solving the same problem for the same audience. The practical differentiators are stack alignment (JavaScript vs Python), multi-agent support (Langflow's CrewAI integration is stronger), and long-term investment signals (Langflow's DataStax backing is a material advantage for enterprise adoption).

For individual developers and small teams prototyping quickly, Flowise's accessibility and community template richness make it an excellent starting point. For teams with Python-first engineering, CrewAI integration needs, or a path toward enterprise deployment, Langflow is the stronger long-term choice.

Neither tool replaces code-first frameworks for maximum architectural flexibility. For complex multi-agent systems that exceed what visual builders can express, see CrewAI vs LangChain and LangChain vs LlamaIndex.

Frequently Asked Questions#

Are Flowise and Langflow actually built on LangChain?#

Both originated as LangChain visual wrappers. Flowise uses LangChain.js; Langflow uses LangChain Python. Both have expanded beyond pure LangChain.

Can non-developers use Flowise or Langflow?#

Both require technical literacy — understanding LLMs, vector stores, and retrieval concepts. For truly non-technical teams, Dify is more appropriate.

Which has a larger community?#

Both have active communities. Flowise has extensive community templates; Langflow has stronger enterprise backing from DataStax.

Can I deploy Flowise or Langflow to production?#

Yes. Both support Docker deployment and expose REST APIs. Production deployments should add monitoring, rate limiting, and fallback logic.

Do these tools support multi-agent workflows?#

Both support agent patterns. Langflow's CrewAI integration gives it stronger multi-agent role-based collaboration support.

What is the main technical difference?#

Stack alignment: Flowise is JavaScript/Node.js; Langflow is Python. This affects extension model, community alignment, and ecosystem fit.