DifyAIA: A Dify Workflow DSL Example Library Built Around a Flask Sidecar

What DifyAIA actually contains, and who it is written for

DifyAIA is a collection of Dify application exports rather than a library you import. The README describes it as a Dify workflow DSL open example library produced by the Bilibili creator 嗯哌AI, with each workflow built and debugged during that creator's own learning process. The repository mixes two kinds of content: self-contained .yml DSL files at the root, such as 思维导图生成助手.yml and 解读Github项目智能机器人.yml, and folders that pair a DSL export with a Python Flask service the workflow calls over HTTP.

The stated audience is narrow and honest about it: developers new to Dify, enterprise teams wanting a reference for standardising workflow conventions, teaching scenarios, and open source contributors. If you already run Dify and understand nodes, variables and HTTP request configuration, the value here is the worked examples, not the code volume. The Flask services are small glue scripts whose job is to receive generated Markdown or text and write a file. The interesting part is the wiring between the two halves, which the README documents step by step for each folder.

The Flask sidecar pattern behind every example

The repeated architecture across Excel_Flask_Dify, DifyMarpFlask_PPT, DifyWordBridg, BillPic2Web, analysis-Github-project and Mindmap-generate-assistant is the same. Dify holds the orchestration: an LLM node produces content, and an HTTP request node posts or gets against a local Flask endpoint. Flask does the thing Dify cannot do natively, which is write a binary or structured file to disk and return a URL.

The Mindmap example shows the full loop in the README's description. The workflow sends Markdown to the Flask service, the service saves it as a .md file, calls Markmap to convert that file into an interactive HTML mind map, and returns a JSON response containing a view link. The user clicks the link. That is a clean division: model output stays text, and the sidecar owns rendering.

BillPic2Web inverts the direction. The user uploads an invoice image and selects an invoice type; the workflow checks whether the selected type matches the type the model reads from the image. On a match it extracts the content and produces a previewable HTML page; on a mismatch it asks the user to confirm the type and retry. That validation step is the most interesting design decision in the repository, because it puts a cheap consistency check in front of an expensive extraction step.

analysis-Github-project chains HTTP requests instead of files: it fetches a GitHub project's README, converts it to plain text, fetches the README structure, and asks an LLM to summarise the project. No Flask service is strictly needed for the fetching, but the README still lists a readme.py service to start.

Installing DifyAIA and running the Word example end to end

There is no package to install. The README's instructions for every folder are the same shape: download the folder to any local directory, open a terminal in that folder, start the Flask service, then import the matching DSL file into Dify and fix the HTTP request node URL. You need a working Dify instance and Python 3 available on the command line.

For DifyWordBridg, the README gives this sequence. First open a terminal at the folder:

cd DifyWordBridg

Then start the Flask service with the script name exactly as the README writes it:

Python3 Doc_flask_app.py

The capital P is how the README spells it; on a case-sensitive shell you will need python3 instead. The service prints a local address, and that address is what you paste into Dify.

Next, in Dify, import the DSL file named in the README:

Dify AI 应用：DifyWordBridg.yml

After import, the README says to modify the LLM node model and modify the URL in the POST request of the HTTP request node. Those are the two edits that make the example run against your environment rather than the author's. The Excel, PPT, invoice and mind map folders follow the same pattern with different script and DSL names, for example python3 Excel_flask_Service.py with the Excel_Flask_Dify.yml file, or Python markmap.py with 思维导图生成助手mindmap_generator.yml.

The mind map folder is the one exception to the single-import flow. The README describes importing the generator DSL, publishing it as a tool, then importing a second DSL that defines an Agent, replacing the model, deleting the pre-wired tool and re-adding the one you just published. If you skip the re-add, the Agent keeps pointing at a tool that does not exist in your workspace.

Where DifyAIA breaks: version drift, ports and the missing licence file

The README states plainly that all workflows were debugged successfully before publication but that errors may appear as Dify versions upgrade and as the choice of large model changes. That is the central limitation, and it is not a small one. A Dify DSL export encodes node types, variable references and model identifiers from the version that produced it. Import into a later Dify and you may get a workflow that loads but fails at a node you did not touch.

The second failure mode is environmental. Every example depends on a Flask service listening on a local address, and the DSL ships pointing at the author's address. If you import without editing the HTTP request node, requests go nowhere. The README calls this out for each folder, which is why the edit step appears in every set of instructions.

There is also a licence ambiguity worth naming. The README says the project uses the MIT open source licence, but the repository root listing shows no LICENSE file, only .gitignore, README.md and the example folders. For a repository you intend to reuse in a commercial product, that gap matters more than the README sentence.

Finally, the repository is a teaching archive, not a maintained product. There are no releases, and the last push was on 2026-07-13. Treat each folder as a snapshot of a working configuration at a point in time.

DifyAIA compared with Dify's own plugin and tool ecosystem

The alternative to this pattern is Dify's own tool and plugin mechanism, which the RELATED SEARCHES around Dify plugin development point at. The difference is where the integration lives.

DifyAIA keeps integration outside Dify. A separate Python process owns file generation, and Dify talks to it over HTTP. You can read, edit and rerun that process without touching Dify, and you can point several workflows at the same service. The cost is that you now operate a second process, manage its port, and keep it running whenever the workflow runs.

A Dify plugin or custom tool moves the integration inside Dify's own extension surface. There is no separate process to babysit and no URL to paste into a node. The cost is that the code has to conform to Dify's plugin interface and to whatever version of that interface your Dify instance supports.

For learning, the sidecar approach in DifyAIA is easier to inspect, because the Python file is short and the HTTP boundary is visible in the DSL. For production, the plugin route removes an operational dependency. Neither is universally better; they fail differently.

Upgrade cost and what the README does not tell you

Upgrading means re-importing DSL files into a newer Dify and re-checking each HTTP request node and LLM node. The README documents no migration procedure, no compatibility matrix, and no list of which Dify version each example was built against. That absence is the real upgrade cost: you cannot tell from the repository whether a given .yml was produced for the version you run.

The Flask scripts are the more stable half. They are plain Python services with no pinned dependency list in the README, so their longevity depends on the libraries they import, which the README does not enumerate. The DSL files are the fragile half.

On licensing, the README asserts MIT. Without a LICENSE file at the root, anyone reusing these workflows in a product should confirm the terms with the author rather than relying on the README line. This is a factual gap, not a legal opinion.

Editorial conclusion

DifyAIA suits developers who are new to Dify and want to read a complete, commented DSL export next to the small Flask service it calls, especially for Excel, Word, PPT, HTML and mind map output. It is a poor fit for anyone who needs a maintained integration: the README states the examples were debugged at publication but may break as Dify versions and models change, no releases are published, and the licence line in the README (MIT) has no LICENSE file at the repository root to confirm it. Before adopting any folder, import the matching .yml into your own Dify instance, open the HTTP request node, and check that the URL and the LLM node model still resolve in your version. If the workflow imports but the request node points at a Flask port nothing is listening on, the failure is in your setup, not in the DSL.

Frequently asked questions

Official sources

1. BannyLon/DifyAIA on GitHub
2. Issues
3. README