Low-Code Agent Platform Comparison: Dify, n8n, Flowise, FastGPT, and Coze Selection Guide

2025 is the year of low-code agent platform explosion. Dify, n8n, Flowise, FastGPT, and Coze — these five names appear in nearly every enterprise's agent selection PPT. "Low-code" sounds simple, but real implementation is full of traps: the gap between visual canvas and production engineering is huge, enterprise RAG and demo are entirely different species. This article compares the five platforms across six dimensions and gives a decision tree for three typical scenarios.

1. Platform Positioning and Target Users

Platform	Positioning	Target Users	Core Philosophy
Dify	LLM application development platform	Full-stack developers + product managers	Treat LLM as backend, do application engineering
n8n	Workflow automation platform (RPA origin)	Business operations + DevOps	Connect everything, AI is just one of the nodes
Flowise	Visual LangChain orchestration	AI engineers + indie developers	Turn LangChain code into canvas
FastGPT	Knowledge base + RAG workbench	Customer support teams + knowledge management	Knowledge base is the agent's brain
Coze	Consumer-grade bot platform	Operations + creators + indie developers	Zero barrier to bot, distribute to Douyin/Feishu

2. Architecture Comparison

Dify: BaaS Architecture

Dify offers hosted (Dify Cloud) and self-hosted (Dify CE/Enterprise) versions, with four-layer architecture: API gateway layer, orchestration engine layer (DSL-based visual workflows similar to Airflow DAG), model gateway layer (abstracts 30+ LLM providers), knowledge base layer (built-in RAG engine).

n8n: Node Editor Architecture

n8n originates from RPA, architecture similar to traditional workflow engines. Node ecosystem is 400+ official and community nodes covering almost all SaaS systems.

Flowise: LangChain Visual Mapping

Flowise's core philosophy is pure: each LangChain concept maps to one canvas node. From LLM Chain to ReAct Agent, from ConversationalRetrievalQA to MultiPromptChain — all LangChain abstractions can be dragged onto canvas.

FastGPT: Knowledge Base First

FastGPT is open-sourced by Labring, with architecture organized around "knowledge base → application". Core innovation: knowledge base as agent brain, all conversational apps default to mounting a knowledge base.

Coze: Consumer Bot Distribution

Coze is ByteDance's 2024-launched consumer bot platform. Biggest differentiator is distribution closed-loop: zero-code visual editor creation side, one-click publish to Feishu, Douyin, WeChat, web, API distribution side, user behavior analytics, dialog logs, A/B testing operations side.

3. Visualization Capability Comparison

Dimension	Dify	n8n	Flowise	FastGPT	Coze
Canvas interaction	DSL nodes + edges	Nodes + edges	Nodes + edges	Nodes + edges	Module cards
Debug capability	Step debug + log	Step execution + history	Step test	Step dialog	Real-time preview
Code extension	Custom code node	Custom function node	Custom JS/Python node	Limited	Not supported
Version management	Draft/publish/history	Manual export	Manual export	Manual export	Built-in
Multi-user collaboration	Roles + permissions	Single tenant	Single tenant	Single tenant	Team space

4. Tool Calling and MCP Support

Platform	Tool calling method	MCP support
Dify	Built-in + OpenAPI import	Native support
n8n	Node as tool	Partial (community nodes)
Flowise	LangChain Tools mapping	Not supported
FastGPT	Custom plugin	Not supported
Coze	Plugin marketplace	ByteDance MCP gateway

5. Knowledge Base and RAG Capability

FastGPT's knowledge base capability is the strongest of the five — supports parent-child segmentation, QA splitting, hybrid retrieval and re-ranking, the first choice for enterprise RAG.

6. On-Prem Deployment and Pricing

Platform	Open-source	Self-hosting difficulty	Commercial pricing
Dify	Apache 2.0	Medium	Enterprise $59/user/month
n8n	Sustainable Use	Easy	Cloud from €20/month
Flowise	MIT	Medium	No commercial version
FastGPT	Apache 2.0	Medium	Enterprise on-demand quote
Coze	Closed source	Not supported	Free + enterprise custom

7. Typical Scenario Selection Decision Tree

Scenario 1 (Customer support bot + enterprise knowledge base): Need on-prem → FastGPT or Dify self-hosted; ByteDance ecosystem → Coze; else → Dify Cloud.

Scenario 2 (RPA / system integration): n8n is almost the only choice, 400+ nodes cover almost all SaaS systems.

Scenario 3 (AI engineer rapid prototyping): Flowise, directly maps LangChain concepts, AI engineers zero learning cost.

Scenario 4 (Consumer bot + distribution): Coze, Douyin/Feishu/WeChat ecosystem distribution.

8. Recommended Combinations

• Startup / indie developer: Dify Cloud + Flowise
• Enterprise IT: Dify self-hosted + n8n self-hosted
• Customer support / knowledge management: FastGPT self-hosted
• Operations / marketing / e-commerce: Coze
• AI engineer team: Dify + direct coding

Final word: there is no "best platform", only "the platform most suitable for your business".

9. Real Production Lessons

In 2024-2025, the author team has implemented Dify and n8n self-hosted deployments for three enterprise clients. Common lessons:

1. Dify's sandbox service must be deployed independently. It's not optional. Production environment must isolate user-uploaded code execution from main API service.
2. n8n's queue mode is mandatory for production. Single instance will block under load. Configure Redis + master-slave + worker nodes.
3. Flowise's "AI engineer zero learning cost" claim only holds if the team already uses LangChain. Otherwise the learning curve is steeper than Dify.
4. FastGPT's PGVector default works for tens of thousands of documents. For million-level documents, migrate to dedicated vector database (Milvus/Qdrant).
5. Coze's data goes to ByteDance cloud. No enterprise will accept this for sensitive business. Use Coze only for internal non-sensitive bots.

10. Common Pitfalls and How to Avoid Them

1. Treating visualization as low-code: drag-and-drop canvas is not low-code. Dify workflows still need lots of parameter configuration (prompts, chunking, model params), non-technical staff learning cost is much higher than expected.

2. Believing self-hosted equals controllable: self-hosting Dify operation cost far exceeds imagination. Each component (vector DB, model gateway, object storage, monitoring, backup) is independent engineering. Small-medium teams should prioritize hosted version.

3. Confusing Coze with "ByteDance's Dify": entirely different species. Coze's core is distribution, not engineering. Using Coze for enterprise training system is mismatch.

4. Believing n8n replaces RPA: n8n is workflow engine, not RPA. It's not good at GUI automation (click, drag, OCR), that part should use UiPath or Playwright.

5. Assuming Flowise suits non-technical staff: Flowise is essentially LangChain visual mapping, requires users to understand LLM Chain, Agent, Tool concepts, not friendly to pure business staff.

11. Migration and Upgrade Considerations

When migrating between platforms (Dify → Coze, n8n → Dify, etc.), key considerations:

1. Data migration: Knowledge bases, document indexes, conversation history. Most platforms do not provide out-of-box migration tools. Plan for one-time data export/import script.
2. Workflow conversion: Dify's DSL and n8n's JSON workflow formats are not directly convertible. Manual re-creation is usually required.
3. User training: Each platform has different UI conventions. Allocate 2-3 days for user training during migration.
4. Cost comparison: Self-hosted costs (servers, DB, ops) vs SaaS subscription. SaaS looks expensive but includes ops, monitoring, scaling.

12. Future Trends in Low-Code Agent Platforms

1. MCP as universal tool layer: All platforms will support MCP Server import, becoming standard.
2. AI-native visualization: Instead of drag-and-drop, use natural language to describe workflows and AI generates canvas.
3. Federated execution: Workflow spans multiple platforms (Dify + n8n + FastGPT cooperating), coordinated via MCP.
4. Edge deployment: Smaller models running on edge devices for low-latency agent applications.