Dify Workflow DSL Files Collection Download

General Introduction

Awesome-Dify-Workflow is an open source GitHub project focused on sharing practical Dify Workflow templates.Dify is a workflow platform that supports the development of large language modeling applications, and this project provides users with a wide range of ready-to-use DSL (Domain Specific Language) files covering translation, data analysis, chart generation, content creation, and other functions. These workflows can be directly imported into a Dify account to help users quickly automate their tasks. The project is maintained by svcvit, and as of April 2025, has accumulated thousands of starmarks and is popular with developers, content creators, and enterprise teams. All templates are free to use, support Dify 0.13.0 and above, and are compatible with features like parallel tasks, session variables, forms, and Echarts rendering.

Below is a table of all the DSL workflows in the project, organized with file names, functional descriptions, and sources to make it easy for users to quickly find the templates they need:

Function List

• Diverse workflow templatesDSL files for translation, diagram generation, intent recognition, content creation, etc. are available to meet different needs.
• free and open source: All workflows are free to download and support users to modify and extend them freely.
• Support for advanced features: Compatible with Dify 0.13.0 for parallel tasks, session variables, forms and Echarts rendering.
• plug-in integration: Enhanced functionality with plug-ins such as MCP Agent, matplotlib, jieba, etc.
• Community collaboration: Through WeChat groups and other platforms, users can exchange experiences and solve problems.
• Easy to import: Copy the DSL file URL and import it into Dify.

Using Help

Installation and use process

Awesome-Dify-Workflow is a repository that depends on the Dify platform and does not require a separate installation. Users need to prepare a Dify account and make sure the version is 0.13.0 or above. Here are the detailed steps:

1. Create a Dify Account
   Visit the Dify website to register for an account. If you choose to deploy locally, refer to the official documentation for installation:

   git clone https://github.com/langgenius/dify.git
   cd dify/docker
   cp .env.example .env
   docker compose up -d
   

   When you're done, visit the http://localhost/install Initialization.

2. Download or get the DSL file
   • show (a ticket) Awesome-Dify-Workflow ProjectThe
   • go into DSL Folder, Browse .yml File List.
   • Find the desired workflow (e.g. json_translate.yml), click for description.
   • Copy the file Raw URL, for example:

     https://raw.githubusercontent.com/svcvit/Awesome-Dify-Workflow/main/DSL/json_translate.yml
     

3. Importing workflows
   • Log in to Dify and go to the Workflow page.
   • Click "Import DSL", paste the Raw URL and confirm the import.
   • Check the workflow configuration to ensure that the model, API key, and dependencies are correct.
4. Configuration environment (as required)
   Some workflows require additional dependencies. For example.matplotlib.yml installation required dify-sandbox-py::
   • compiler /docker/volumes/sandbox/dependencies/python-requirements.txt, add:

     matplotlib
     pandas
     

   • Restart the sandbox:

     docker compose restart sandbox
     

Featured Function Operation

The following describes in detail the operation process of several core workflows:

1. JSON translation workflow (json_translate.yml)

• functionality: Translates the content of the JSON file, preserving the original structure.
• procedure::
  1. import (data) json_translate.ymlThe
  2. Configure the input node to set the source language (e.g., Chinese), target language (e.g., English), and JSON data.
  3. Selecting a Translation Model (Recommended) DeepSeek or OpenAI), set the API key in Dify.
  4. Run the workflow to output the translated JSON.
  5. If the string exceeds the limit, modify the .env::

     CODE_MAX_STRING_LENGTH=2000000
     TEMPLATE_TRANSFORM_MAX_LENGTH=2000000
     

     Restart the container:

     docker compose down && docker compose up -d
     

2. Graph generation workflow (matplotlib.yml, chart_demo.yml)

• functionality: Generate bar charts, line charts, etc., output Base64 images or Echarts charts.
• procedure::
  1. import (data) matplotlib.yml maybe chart_demo.ymlThe
  2. assure dify-sandbox-py Installed, contains matplotlib cap (a poem) pandasThe
  3. Input data (CSV or manual input).
  4. Configure chart types and styles (e.g., colors, labels).
  5. Run the workflow to see the Base64 image or Echarts rendering.
  6. If combined with a database, configure SQL query nodes to dynamically generate charts.

3. Intent recognition chatbots (根据用户的意图进行回复.yml)

• functionality: Determine the intent based on user input and select a reply path.
• procedure::
  1. import (data) 根据用户的意图进行回复.ymlThe
  2. Set up intent classification rules (keywords or model predictions).
  3. Configuration dialog modeling and short-term memory variables.
  4. Test inputting different questions and observe the path switching.
  5. Adjust the style of discourse (e.g., humor or professionalism).

4. Chinese New Year scroll generator (春联生成器.yml)

• functionality: Generate personalized spring scrolls for the holidays.
• procedure::
  1. import (data) 春联生成器.ymlThe
  2. Make sure the system fonts support Chinese (e.g. Song).
  3. Enter a theme (e.g. "New Year") or keywords.
  4. Run the workflow to output the spring text.
  5. Optional: output the Spring Festival image with background in combination with the image generation node.

5. Data analysis workflow (数据分析.7z)

• functionality: Query databases, generate reports and charts.
• procedure::
  1. decompression (in digital technology) 数据分析.7zThe DSL file is imported.
  2. Configure the database connection (e.g. MySQL).
  3. Enter a query statement to set the chart type.
  4. Run workflows and output analysis results and visualization charts.
  5. If you need a Flask service, deploy the server-side code.

caveat

• network problem: If you get an error when importing a DSL (e.g., the Connection refused), check the network or retry later.
• Dependency management: Use dify-sandbox-py mounting numpy,scikit-learn and other libraries to avoid permission problems.
• version compatibility: Ensure that Dify version is 0.13.0 or higher to support Agent nodes and parallel tasks.
• Knowledge base processing: PDF upload garbled when the recommended use of the MinerU Convert to Markdown and then import.

application scenario

1. content creation
   Available to self-publishers Dify 运营一条龙.yml maybe Text to Card Iteration.yml, generating copy and images for platforms like Little Red Book and Jitterbug to improve efficiency.
2. data visualization
   Analysts available matplotlib.yml maybe 数据分析.7zThe data is converted into charts and graphs, which are suitable for report production.
3. multilingual translation
   Cross-border teams can be reached through json_translate.yml maybe 全书翻译.yml, batch translation of documents or product descriptions.
4. client service
   Enterprise deployable 根据用户的意图进行回复.yml, realizing intelligent customer service and reducing labor costs.

QA

1. What if the DSL import fails?
   Check the URL format to make sure the network is working, or download manually! .yml File Upload.
2. How to deal with dependency library installation errors?
   utilization dify-sandbox-pyin python-requirements.txt Add the library name and restart sandbox.
3. How to implement timed tasks?
   combining dify-schedule project, configure triggers, and set run intervals.
4. PDF Knowledge Base garbled how to do?
   Use MinerU or other tools to convert PDF to Markdown format and re-upload it.