head to head · open source

Dify vs cangjie-skill

Dify has 156,215 GitHub stars, 24,668 forks, 1,067 open issues and last shipped today. cangjie-skill has 10,225 stars, 1,185 forks, 23 open issues and last shipped 5 days ago. Dify leads on adoption by 1,428% (156,215 vs 10,225 stars). Dify is written in TypeScript under a custom or non-standard licence; cangjie-skill is written in Python under MIT. Dify has attracted 16% as many forks as stars, cangjie-skill 12%. Dify was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (automation), so they are genuine substitutes rather than adjacent tools.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

Dify ★ 156K cangjie-skill ★ 10K category AI & Machine Learning

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Side by side

Dify cangjie-skill
GitHub stars ★ 156K ★ 10K
License Custom / other MIT
Written in TypeScript Python
Last push 2026-09-18 2026-09-13
Forks ⑂ 25K ⑂ 1.2K
Self-hosting Yes Yes
Data ownership Your server Your server

pick Dify if

  • You weight community size — 156K stars and counting
  • You want the Custom / other license terms
  • Your stack matches TypeScript
  • You value the larger contributor base for long-term maintenance

full Dify profile →

pick cangjie-skill if

  • You want the cangjie-skill feature set and don't need the biggest community
  • You prefer the MIT license terms
  • Your stack matches Python
  • You evaluated both and cangjie-skill fits your workflow better

full cangjie-skill profile →

About Dify

Dify is an open source LLM application development platform built in TypeScript. It provides a visual interface for designing, testing, and deploying AI workflows, RAG pipelines, and agentic systems without writing extensive code. The platform lives in the generative AI development ecosystem and targets teams needing to move from prototype to production using consistent tooling across environments.

read the full Dify overview →

About cangjie-skill

Cangjie Skill is an MIT licensed Python tool that distills methodologies from books, long form videos, and podcasts into callable AI Skills, built for developers and knowledge workers who assemble agent workflows on OpenClaw, Claude Code, or DeepSeek Harness.

read the full cangjie-skill overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 247K Ollama ★ 181K Open WebUI ★ 152K langchain ★ 147K ponytail ★ 141K

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dify vs langchain dify vs ponytail dify vs graphify dify vs claude-mem dify vs ragflow langchain vs ponytail langchain vs graphify ponytail vs graphify openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai llama-cpp vs vllm ollama vs pageindex ollama vs langfuse

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Compare either of these against the rest of the AI Development Platforms field.

Dify vs langchain Dify vs ponytail Dify vs generative-ai-for-beginners Dify vs graphify Dify vs claude-mem Dify vs ragflow Dify vs PaddleOCR Dify vs Agent-Reach Dify vs headroom Dify vs Mem0 Dify vs daily_stock_analysis Dify vs LiteLLM

Frequently asked questions

Is Dify or cangjie-skill more popular?

Dify has 156,215 GitHub stars and cangjie-skill has 10,225. Dify has the larger community by that measure.

Are Dify and cangjie-skill free?

Both are open source. Dify has no licence declared in this registry, and cangjie-skill is licensed under MIT. Both are free to self-host.

What is the difference between Dify and cangjie-skill?

Dify is written in TypeScript and cangjie-skill in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, Dify or cangjie-skill?

Choose Dify if you want the larger community (156,215 stars) or its Custom / other licence terms. Choose cangjie-skill if its feature set, stack or MIT licence fits better. Both are self-hostable.