---
title: "Dify vs Flowise vs Langflow: Low-Code AI Agent Platforms Compared"
author: "Nate Laquis"
author_role: "Founder & CEO"
date: "2030-05-11"
category: "Technology"
tags:
  - Dify vs Flowise
  - Langflow comparison
  - low-code AI agents
  - AI workflow builder
  - no-code AI platform
excerpt: "Three platforms promise drag-and-drop AI agents. But they differ wildly in architecture, RAG quality, and production readiness. Here is what actually matters when choosing between them."
reading_time: "14 min read"
canonical_url: "https://kanopylabs.com/blog/dify-vs-flowise-vs-langflow"
---

# Dify vs Flowise vs Langflow: Low-Code AI Agent Platforms Compared

## Why Low-Code AI Platforms Are Everywhere in 2030

Every week, another startup or enterprise team discovers that building AI agents from scratch is painful. You need to wire up LLM providers, manage vector databases, handle prompt versioning, build conversation memory, orchestrate multi-step tool calls, and deploy the whole thing reliably. That is months of engineering work before you even validate whether the agent solves a real problem.

Low-code AI agent platforms collapse that timeline from months to days. Dify, Flowise, and Langflow are the three open-source leaders in this space, and each takes a fundamentally different approach. Dify is a full-stack LLMOps platform with a polished web UI. Flowise is a lightweight, Node.js-based builder focused on LangChain workflows. Langflow started as a visual frontend for LangChain and has evolved into a Python-native agent builder with its own runtime.

Choosing between them is not a matter of which logo looks nicer. The decision affects how your team builds RAG pipelines, how you handle agent orchestration, whether you can self-host without DevOps overhead, and what happens when you outgrow the visual builder. We have helped multiple clients evaluate and deploy all three platforms, and the differences are more significant than any feature comparison table suggests.

![Developer building low-code AI agent workflows on a visual platform](https://images.unsplash.com/photo-1555949963-ff9fe0c870eb?w=800&q=80)

If you have already compared workflow automation tools like [n8n, Make, and Zapier](/blog/n8n-vs-make-vs-zapier), this decision follows similar logic. The cheapest option is not always the best. The most feature-rich option is not always the most practical. What matters is alignment between the platform's strengths and your team's actual needs.

## Platform Overviews: What Each Tool Actually Is

### Dify

Dify is the most ambitious of the three. It positions itself as an "LLMOps platform" rather than just a workflow builder, and that framing is accurate. You get a visual workflow editor, a built-in RAG pipeline with document ingestion, prompt management with versioning, API publishing, user analytics, and a web-based chat interface you can embed directly into products. The team behind Dify (backed by significant VC funding) ships updates aggressively, with new features landing almost weekly.

Dify's architecture is Python-based with a React frontend. It uses PostgreSQL for metadata, Redis for caching, and supports multiple vector databases (Weaviate, Qdrant, Pinecone, pgvector, and others). The platform feels like a product, not a developer tool. Non-technical team members can use the prompt editor and test chat interfaces without touching code.

### Flowise

Flowise is the scrappy, developer-friendly option. Built on Node.js with a drag-and-drop interface powered by React Flow, it is essentially a visual way to compose LangChain.js and LlamaIndex chains. If you have used LangChain in code, Flowise will feel instantly familiar because every node maps to a LangChain component. Document loaders, text splitters, embeddings, vector stores, chains, agents, and tools are all represented as draggable nodes.

Flowise is lightweight by design. It runs on SQLite by default (with PostgreSQL and MySQL support), requires minimal resources, and can be deployed on a $5/month VPS. The trade-off is that it has fewer built-in features than Dify. There is no native prompt versioning, no built-in analytics, and the RAG pipeline requires more manual configuration.

### Langflow

Langflow was originally a UI wrapper around LangChain Python, but it has matured into something more substantial. Now maintained by DataStax, Langflow offers a visual flow editor where you connect components to build AI pipelines. The Python-native runtime means you get access to the entire Python AI ecosystem, including LangChain, LlamaIndex, Hugging Face transformers, and custom Python functions.

Langflow's standout feature is its component system. You can create custom Python components that appear as drag-and-drop nodes in the visual editor. This bridges the gap between low-code convenience and code-level flexibility better than either Dify or Flowise. The platform also includes a built-in playground for testing flows and an API layer for deployment.

## Visual Builder Comparison: Drag-and-Drop Experience

All three platforms offer visual, node-based editors. But the editing experience varies dramatically in practice.

### Dify's Workflow Editor

Dify offers two modes: a simple "chatbot/completion" mode where you configure a single prompt with variables, and an advanced "workflow" mode with a full node graph. The workflow editor supports conditional branching, loops, parallel execution, variable passing, and HTTP request nodes. It is the most feature-complete visual editor of the three. The UI is polished, with clear node labels, inline variable previews, and a step-by-step debug view that shows exactly what each node produced during execution.

The downside is complexity. New users often struggle to understand the difference between Dify's app types (chatbot, text generator, agent, workflow). The workflow editor has a learning curve, especially around variable scoping and iteration nodes. But once you understand the mental model, it is genuinely powerful for building multi-step AI pipelines without code.

### Flowise's Canvas

Flowise uses a straightforward node graph. You drag components from a sidebar, connect outputs to inputs, and configure each node's parameters in a side panel. It is simpler than Dify's editor because there are fewer concepts to learn. Every node is a LangChain or LlamaIndex component, and the connections represent data flow between those components.

The simplicity is both Flowise's strength and its limitation. Building a basic RAG chatbot takes 5 minutes: drag a document loader, text splitter, embeddings model, vector store, and conversational chain, connect them, and you are done. But complex logic (conditional routing, error handling, parallel processing) requires workarounds or custom code nodes. Flowise does not have native branching or loop support in the visual editor.

### Langflow's Flow Editor

Langflow's editor sits between Dify and Flowise in complexity. The node graph is clean, with color-coded component categories and a smart connection system that only allows compatible types to link. Langflow recently added conditional routing and a "flow as component" feature that lets you nest entire flows as single nodes, which is excellent for managing complexity in large pipelines.

Where Langflow excels is the code-to-visual bridge. You can write a Python component, and it automatically appears as a configurable node with typed inputs and outputs. This means your data scientists can build custom ML preprocessing steps, and your less technical team members can incorporate those steps into visual flows without understanding the Python code. None of the other platforms handle this transition as cleanly.

![Analytics dashboard showing AI agent workflow performance metrics](https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=800&q=80)

## RAG Support and Vector Database Integration

Retrieval-Augmented Generation is the most common use case for all three platforms. The quality of RAG support determines whether your AI agent gives accurate, grounded answers or hallucinates confidently.

### Dify's RAG Pipeline

Dify has the most opinionated and complete RAG implementation. You upload documents through a web UI, and Dify handles chunking, embedding, and indexing automatically. It supports PDF, DOCX, TXT, Markdown, CSV, and web page scraping out of the box. The chunking strategy is configurable (by paragraph, fixed size, or custom separator), and you can preview chunks before indexing.

Dify also supports a "knowledge base" abstraction that groups related documents together. You can attach multiple knowledge bases to a single app, set retrieval parameters (top-k, score threshold, reranking model), and configure hybrid search that combines keyword matching with vector similarity. The built-in reranking support (using Cohere or Jina rerankers) is a genuine differentiator. Most RAG implementations skip reranking entirely, which hurts retrieval quality.

The limitation is flexibility. Dify's RAG pipeline works great for standard document Q&A, but customizing the retrieval logic (for example, adding metadata filtering, implementing recursive retrieval, or chaining multiple retrieval strategies) requires working within Dify's abstractions rather than writing custom code.

### Flowise's RAG Setup

Flowise gives you granular control over every RAG component because each piece is a separate node. You choose your document loader (PDF, web scraper, Notion, GitHub, etc.), your text splitter (recursive character, token-based, markdown header), your embedding model, and your vector store independently. This modularity means you can swap any component without rebuilding the entire pipeline.

Flowise supports a wide range of vector databases: Pinecone, Weaviate, Chroma, Qdrant, Supabase, pgvector, Milvus, Zep, and others. The setup requires more clicks than Dify (you are configuring each node individually), but the flexibility is worth it for teams that need non-standard RAG architectures. Multi-index retrieval, metadata-filtered search, and parent-document retrieval are all possible by connecting the right nodes.

### Langflow's RAG Approach

Langflow supports RAG through a combination of built-in components and the underlying LangChain/LlamaIndex libraries. The component library includes document loaders, text splitters, embeddings, and vector stores similar to Flowise. Where Langflow differs is in the ability to create custom retrieval components in Python. If you need a specialized retrieval strategy (like query decomposition, step-back prompting, or multi-hop retrieval), you write a Python component and plug it into the visual flow.

Langflow also integrates with DataStax Astra DB (unsurprisingly, given the DataStax backing), which provides a managed Cassandra-based vector database. For teams already in the DataStax ecosystem, this tight integration simplifies deployment. For everyone else, Langflow supports the same vector databases as Flowise through the LangChain integration layer.

## Agent Capabilities, LLM Provider Support, and Self-Hosting

### Agent Orchestration

AI agents that use tools, make decisions, and take multi-step actions are the highest-value use case for these platforms. Dify supports agent mode with function calling, where the LLM can invoke built-in tools (web search, code execution, API calls) or custom tools defined via OpenAPI specs. The agent loop handles tool selection, execution, observation, and iteration automatically. Dify also supports multi-agent workflows where different agents handle different sub-tasks.

Flowise supports LangChain agents (ReAct, OpenAI Functions, Plan-and-Execute) through its node system. You define tools as nodes, connect them to an agent node, and the LangChain agent runtime handles the orchestration. The agent support is solid for standard patterns but limited when you need custom agent logic. There is no native multi-agent support, though you can chain separate Flowise flows via API calls.

Langflow offers the most flexible agent support because you can write custom agent logic in Python. The built-in agent components cover standard patterns, but the real power is creating custom components that implement specialized agent behaviors: tree-of-thought reasoning, self-reflection loops, or domain-specific decision frameworks. If your agent needs to do something non-standard, Langflow gives you the escape hatch.

### LLM Provider Support

All three platforms support the major LLM providers, but the breadth of support differs:

- **Dify:** OpenAI, Anthropic, Google (Gemini), Azure OpenAI, AWS Bedrock, Ollama, Groq, Mistral, Cohere, Hugging Face, Zhipu, Minimax, and dozens more. Dify has the widest native provider support. Model configuration is centralized, so switching providers requires changing one setting rather than reconfiguring every node.

- **Flowise:** OpenAI, Anthropic, Google, Azure OpenAI, Ollama, Groq, Mistral, Replicate, Hugging Face, and others via LangChain integrations. Provider support is node-based, meaning you swap the LLM node to change providers. Good selection, though slightly narrower than Dify for niche providers.

- **Langflow:** Supports anything LangChain supports, plus custom integrations via Python components. If an LLM has a Python SDK, you can use it in Langflow. The built-in component library covers all major providers, and the DataStax partnership adds optimized Astra DB vector search integration.

### Self-Hosting

All three platforms are open source and self-hostable, but the operational burden varies significantly.

**Dify** requires Docker Compose with PostgreSQL, Redis, and optionally a vector database. The default setup includes 5+ containers (API server, web frontend, worker, database, Redis, sandbox). Resource requirements are the highest of the three: plan for at least 4GB RAM and 2 CPU cores for a basic deployment. The team provides official Docker Compose files and Helm charts for Kubernetes.

**Flowise** is the easiest to self-host. A single Node.js process with SQLite runs on a 1GB RAM VPS. You can deploy with a one-line npx command, a Docker container, or directly on Railway, Render, or any platform that supports Node.js. For small to medium workloads, Flowise's operational simplicity is hard to beat.

**Langflow** runs as a Python application with moderate resource requirements. The DataStax-backed version includes a managed cloud option, but self-hosting requires Python 3.10+, pip, and optionally PostgreSQL for production use. Resource requirements sit between Flowise and Dify: 2GB RAM is comfortable for most workloads.

## Pricing and When to Use Low-Code vs Custom Development

All three platforms are open source, so the base software is free. The real cost comes from infrastructure, managed cloud plans, and engineering time.

### Managed Cloud Pricing

- **Dify Cloud:** Free tier (200 GPT-4 message credits), Plus at $59/month (5,000 credits, unlimited apps), Professional at $159/month (unlimited credits, priority support). Enterprise pricing is custom. LLM API costs are separate.

- **Flowise Cloud:** Starter at $35/month (5,000 predictions), Pro at $65/month (25,000 predictions), Enterprise at custom pricing. Flowise Cloud is the most affordable managed option for moderate usage.

- **Langflow (DataStax):** Free tier with limited executions, Pro plans starting around $50/month. Pricing is tied to compute minutes and Astra DB usage. The managed offering bundles vector database hosting, which simplifies cost estimation for RAG-heavy workloads.

### Self-Hosted Cost Comparison

For self-hosted deployments, infrastructure costs break down roughly as follows. Flowise on a $10 to $20/month VPS handles most small-to-medium workloads. Langflow on a $20 to $40/month server covers typical production use. Dify on a $40 to $80/month server (due to PostgreSQL, Redis, and multiple containers) is the most expensive to run, but includes the most features out of the box. All three still require separate LLM API costs, which typically dwarf the infrastructure costs.

### When Low-Code Platforms Make Sense

Low-code AI platforms are the right choice when you need to validate an AI use case quickly, when your team includes non-developers who need to configure AI workflows, or when the use case fits cleanly into the RAG chatbot or simple agent pattern. They are also excellent for internal tools where polish matters less than speed to deployment.

They are the wrong choice when you need tight integration with existing systems, custom UX that goes beyond a chat interface, complex agent orchestration with domain-specific logic, or when latency and cost optimization are critical at scale. In those cases, [custom development delivers what AI builders cannot](/blog/ai-app-builders-vs-custom-development).

![Code on a monitor showing custom AI agent development workflow](https://images.unsplash.com/photo-1461749280684-dccba630e2f6?w=800&q=80)

The best approach for many teams is a hybrid strategy. Use a low-code platform to prototype and validate the AI workflow, then migrate to custom code for production if the use case proves valuable. This is exactly the pattern we see with clients who start on Dify or Flowise and later move to a custom-built solution when they need more control. Our guide on [building an AI workflow builder](/blog/how-to-build-an-ai-workflow-builder) covers what that custom path looks like in practice.

## The Verdict: Which Platform Should You Choose

After deploying all three platforms in production environments, here is how we recommend deciding.

### Choose Dify If:

- You want the most complete, all-in-one platform with RAG, agents, prompt management, and analytics built in.

- Your team includes non-technical users who need to build and manage AI workflows through a polished web UI.

- You are comfortable with higher infrastructure requirements in exchange for more features.

- You need strong document processing and knowledge base management out of the box.

### Choose Flowise If:

- You want the simplest, fastest setup with minimal infrastructure overhead.

- Your team is comfortable with LangChain concepts and wants visual composition of LangChain components.

- You are running on constrained infrastructure (small VPS, edge deployments) where resource efficiency matters.

- You need a lightweight solution for internal chatbots or customer-facing RAG applications.

### Choose Langflow If:

- Your team includes Python developers who want to extend the platform with custom components.

- You need the flexibility to implement non-standard agent logic, retrieval strategies, or ML preprocessing steps.

- You are in the DataStax ecosystem and want tight Astra DB integration.

- You want the best bridge between visual, low-code building and full code-level control.

For most teams evaluating their first low-code AI platform, Dify offers the gentlest learning curve with the most built-in capability. For developer-heavy teams that value simplicity, Flowise gets you to production fastest. For teams that know they will need custom Python logic eventually, Langflow avoids the pain of migrating later.

None of these platforms replace custom development for complex, production-grade AI systems. They are prototyping and validation tools that can grow into lightweight production systems for the right use cases. If you are building AI-powered features that are core to your product's value proposition, you will eventually need dedicated engineering. But starting with a low-code platform to prove the concept is almost always the right first step.

If you are not sure which approach fits your use case, or you have outgrown your current low-code setup and need a custom solution, we can help you evaluate the options and build what is right for your team. [Book a free strategy call](/get-started) and we will walk through your specific requirements together.

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*Originally published on [Kanopy Labs](https://kanopylabs.com/blog/dify-vs-flowise-vs-langflow)*