dify vs n8n
The two are close in size: 159.0k stars for dify, 208.1k for n8n. Over the days we have tracked them dify moved +6.1% and n8n +5.3%, so dify is growing faster right now.
n8n leads on context depth and noise control. dify does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Integrated low-code workspace combining visual AI workflows, RAG pipelines, agent builders, and broad multi-provider model support that can be self-hosted.
- Stars
- 159.0k
- Tracked growth
- +6.1%
- Maturity
- ●●●●●
- Last commit
- 12m ago
- Language
- Python
- License
- NOASSERTION
- Cost to run
- Free cloud sandbox (200 GPT-4 calls); self-hosted on your infra.
Provides a visual low-code canvas plus custom code and 1,500+ integrations to build production-ready multi-step AI agents with self-hosting and no provider lock-in.
- Stars
- 208.1k
- Tracked growth
- +5.3%
- Maturity
- ●●●●●
- Last commit
- 12m ago
- Language
- TypeScript
- License
- NOASSERTION
- Cost to run
- Self-hostable; cloud offering available
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | dify | n8n |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Diff/file only | ●●●●● Workflow-level |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● None mentioned | ●●●●● Human approvals |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Prompt IDE & tools | ●●●●● Highly extensible |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable / local | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-key / any | ●●●●● Provider-agnostic |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Docker Compose quickstart | ●●●●● One-command start |
Which one to pick
Pick dify if…
Easy deployment — run a full-featured, visual LLM app and agent platform locally or in your VPC using Docker Compose (or use Dify Cloud for a sandbox with free GPT‑4 calls).
Pick n8n if…
Model flexibility — pick OpenAI, Anthropic, Google, or open-source models and self-host the platform while composing agents visually and adding custom code.
- Context depth: Workflow-level (3/5 against 1/5)
- Noise control: Human approvals (3/5 against 1/5)
What people want from each one
langgenius/dify
Hacker News: Dify, a visual workflow to build/test LLM applications drew 185 points and 38 comments.
Questions people ask
Is dify better than n8n?
n8n leads on context depth and noise control. dify does not take any axis by a clear margin. dify is worth picking when easy deployment — run a full-featured, visual LLM app and agent platform locally or in your VPC using Docker Compose (or use Dify Cloud for a sandbox with free GPT‑4 calls).
Which of dify and n8n keeps my code private?
dify: Self-hostable / local (5/5). n8n: Self-hostable (4/5).
What does each one cost to run?
dify: Free cloud sandbox (200 GPT-4 calls); self-hosted on your infra.. n8n: Self-hostable; cloud offering available.
Full profiles: langgenius/dify and n8n-io/n8n. Everything else in Low-code builders.