dify vs n8n
The two are close in size: 154.5k stars for dify, 203.4k for n8n. Over the days we have tracked them dify moved +3.6% and n8n +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
- 154.5k
- Tracked growth
- +3.6%
- Maturity
- ●●●●●
- Last commit
- 27m ago
- Language
- TypeScript
- 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
- 203.4k
- Tracked growth
- +3%
- Maturity
- ●●●●●
- Last commit
- 22m 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.
n8n-io/n8n
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.