Dify: review, pricing, security and alternatives

What Dify does

Dify belongs to the automation family: platforms, often visual, that chain steps, apps and models into workflows with or without an agent. On our grid it ranks #5 of the 26 projects of this family listed in the directory.

You use it from: web app and API. Usable models: Claude (Anthropic), GPT (OpenAI), Gemini (Google), DeepSeek, Qwen (Alibaba), Mistral, Llama (Meta), local models (Ollama, llama.cpp, vLLM…), and any OpenAI-compatible model. It can run on your own machine or server.

Who is Dify for?

It is for teams that want to connect their tools (CRM, e-mail, spreadsheets) and hand the AI the steps that require reading or deciding. Typical tasks people give it:

• qualify incoming requests and route them
• extract data from documents into a spreadsheet
• generate periodic reports

It is a good candidate if you prefer doing everything in a browser, with no install, your data must stay on your machines, and you want the option of a local model, with no API fees.

Access and human control

Its documentation does not detail the access granted to the agent: check the settings before connecting it to your accounts.

We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.

Dify pricing

The code is public but the repository declares no licence: reuse is not formally allowed. The real cost comes from model calls: each loop step sends the whole context back to the model, and a task of several dozen steps can cost far more than a single question. The LLM price calculator estimates it for the chosen model.

Getting started with Dify

1. Install it: the project ships as a Python package (install with pip or uv). The exact command is in the repository README.
2. Pick the model: an API key from a provider, or a local model if your machine has enough graphics memory (the VRAM calculator tells you).
3. Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
4. Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.

Security: risk level

A workflow runs unattended: limit the credentials it holds and log what it sends.

Points found for Dify:

• No licence: legally shaky for company use.

Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.

Project activity and releases

Created on April 12, 2023, the langgenius/dify repository has 159,425 stars, 25,193 forks and 488 open issues, with 14,429 commits in total. It received changes this month.

Over the last 52 weeks: 9,551 commits, an average of 183.7 per week.

171 releases in total; 23 in the last twelve months. Latest release: 1.18.0, on October 23, 2026.

Dify vs other automation

GitHub stars of the best-rated open-source projects in the automation family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.

Dify and other automation at a glance

The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.

How we rated it

Score: 83/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 38/40: 159,425 stars on a log scale (40 points from about 200,000).
• Activity 30/30: last commit less than a month ago, 9 releases in six months, 9,551 commits in a year.
• Framework 15/30: licence, default human approval, self-hosting, model choice and documentation site.

The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.