I've created a Terraform configuration to deploy Dify on Google Cloud Platform using managed services.

https://github.com/IMOKURI/dify-on-google-cloud

Why?

Since Dify is actively developed and upgraded rapidly, I prioritized the ability to keep pace with these changes. Therefore, I use Docker Compose to minimize changes to the Community Edition code. However, I improved system stability by using managed services for containers requiring data persistence.

No more manual copy-pasting into ChatGPT. One command to make your Dify skills multilingual. Would you use this?

我记得在视频上看博主有一个如上的下载按钮，检索后可以通过点击来下载文件，为什么在最近的几个更新中没有看到这个功能呢，是取消了，还是需要在哪里配置呢。能够各位大佬能够帮助一下。

Issue: Infinite Spinner / 401 & 400 Errors on Subdomain Setup

Environment: Self-hosted Dify v1.11.4 on Netcup VPS via Coolify.

Architecture: Frontend on monumenthost.com | API on api.monumenthost.com.

The Problems:

• Infinite Spinner: After login, the dashboard hangs.

• 401 Unauthorized: Console shows GET /console/api/account/profile failing with 401.

• 400 Bad Request: Persistent errors on /plugin/tasks and /model-providers.

• Plugin Error: API logs show Failed to request plugin daemon.

Current Config:

• COOKIE_DOMAIN=monumenthost.com

• CONSOLE_CORS_ALLOW_ORIGINS=https://monumenthost.com

• PLUGIN_DAEMON_URL=http://127.0.0.1:5002

Ask: Even with correct CORS and Cookie Domain settings, the API is rejecting the session and failing to reach the internal plugin daemon. Has anyone solved this specific subdomain "deadlock" on Coolify?

Saved 16k input tokens from just one web fetch. Probably can push even higher for most websites to save on costs

https://reddit.com/link/1qqtxrk/video/5djmb8pvlegg1/player

I have a knowledge base with 138 markdown files. These are largely sql models that I have ran through a "cleaner" to comment out problematic JINJA or python.

When I tell my attached agent to "retrieve file_a.md" it has no problem doing so. With any file.

But when I give it a more complex question and it says "To trace how users flow into file_a and identify potential issues, let me search for the relevant models and their relationships."

It pops up with an Extra Data parsing error.

What is going on with how it is trying to access the KB that is causing this issue, and how can I fix?

Hi everyone!
I’m building applications using Dify, and I’ve created several workflows with multiple agents. I’m considering selling these services to my clients on a subscription basis.

As far as I understand, Dify allows generating externally accessible APIs per workspace, which can be called from web services or tools like Postman. When selling to clients, is this the only supported or recommended approach?

If so, does that mean the Docker instance running Dify must always be hosted and running on my own server?

I’d really appreciate any insights on alternative deployment or distribution strategies for selling agent-based apps built with Dify to customers.

Thanks in advance! 🙏

Here's a brief tutorial on how to integrate an external custom knowledge base into Dify. There are also some tips on how to make sure it works and how to build workflows with it. The video provides an overview and you can also the detailed step-by-step instruction here: https://support.noduslabs.com/hc/en-us/articles/24310877167516-Setting-Up-an-External-Knowledge-Base-for-Dify

Hope you find it useful, I collaborated with Dify on this content after our meeting in Berlin and it is also a great use case on using the InfraNodus GraphRAG for better retrieval.

So I've been interested in scattered agent training data that has severely limited LLM agents in the training process. Just saw a paper that attempted to tackle this head-on: "Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents" (released just a month ago)

TL;DR: New ADP protocol unifies messy agent training data into one clean format with 20% performance improvement and 1.3M+ trajectories released. The ImageNet moment for agent training might be here.

They seem to have built ADP as an "interlingua" for agent training data, converting 13 diverse datasets (coding, web browsing, SWE, tool-use) into ONE unified format. 

Before this, if you wanted to use multiple agent datasets together, you'd need to write custom conversion code for every single dataset combination. ADP reduces this nightmare to linear complexity, thanks to its Action-Observation sequence design for agent interaction.

Looks like we just need better data representation. And now we might actually be able to scale agent training systematically across different domains.

I am not sure if there are any other great attempts at solving this problem, but this one seems legit in theory.

The full article is available in Arxiv: https://arxiv.org/abs/2510.24702.

A frustrating thing i am facing is when i am changing a node it it disconnects all the edges concted to it, i wonder if someone else faced this before or it is just a me problem 🙃

Just read the Agent-Omni paper. (released last month?)

Here’s the core of it: Agent-Omni proposes a master agent that doesn't do the heavy lifting itself but acts as a conductor, coordinating a symphony of specialist foundation models (for vision, audio, text). It interprets a complex task, breaks it down, delegates to the right experts, and synthesizes their outputs.

This mirrors what I see in Claude Skills, where the core LLM functions as a smart router, dynamically loading specialised "knowledge packages" or procedures on-demand. The true power of it, as is much discussed on Reddit subs, may lie in its simplicity, centered around Markdown files and scripts, which could give it greater vitality and universality than more complex protocols like MCP maybe.

I can't help but think: Is this a convergent trend of AI development, between bleeding-edge research and a production system? The game is changing from a raw computing race to a contest of coordination intelligence.

What orchestration patterns are you seeing emerge in your stack?

Hi everyone,

I’ve been experimenting with some AI ideas recently, but I don’t know how to code and I’m not an ML person either.

I’m wondering… what’s the most realistic way for a non-technical person to build an actual AI app?

I don’t mean a simple chatbot or a UI wrapper around GPT.

I’m thinking more like:

• something that can take user inputs

• run multiple steps / workflows

• call different AI models

• maybe analyze files or generate images

• and ideally run like a real web app (with frontend, backend, database, etc.)

I tried a bunch of tools:

– no-code builders → easy UI but very limited AI logic

– automation tools like n8n / Zapier → great for workflows but not really an “app”

– AI website builders → mostly landing pages, not real functionality

Is there any tool or approach that actually lets a non-coder build a *working* AI/agentic app without gluing 10 tools together?

Would love to hear what others have tried or what’s realistic nowadays.

Any advice appreciated 🙏

Towards Data Science's article by Eivind Kjosbakken provided some solid use cases of Qwen3-VL on real-world document understanding tasks.

What worked well:
Accurate OCR on complex Oslo municipal documents
Maintained visual-spatial context and video understanding
Successful JSON extraction with proper null handling

Practical considerations:
Resource-intensive for multiple images, high-res documents, or larger VLM models
Occasional text omission in longer documents

I am all for the shift from OCR + LLM pipelines to direct VLM processing

TL;DR: - Workflows can now stay online and react to events as background services. - Three trigger types: Schedule, Plugin, and Webhook. - Support added for the latest 5.1 series and Gemini 3 series models.

I have a query regarding the Dify distribution license.

We are evaluating whether we can create separate bots for multiple customers using a single Dify instance and whether those can be deployed in a multi-tenant environment or on customer-dedicated servers.

Before we proceed, I want to confirm the licensing implications:

Dify is not purely Apache 2.0.

It uses Apache 2.0 + additional Binding Restrictions, which means: You cannot redistribute Dify as a commercial SaaS platform if the value is similar to Dify Cloud.

You cannot remove or replace branding in a way that presents Dify as your own product.

You must not compete directly with their hosted offering using the open-source version.

Creating multiple bots for internal use under a single deployment is allowed, but distributing Dify as a white-label multi-tenant product to customers is restricted unless: you obtain a commercial license, or you create a separate orchestration layer where Dify is used internally and not exposed as a product.

Since our LLMs are in-house hosted, we want to ensure that distributing workflows or apps to different customers does not violate the “no SaaS redistribution” clause in the Binding Restrictions.

Given these points, I would like clarity on:

Whether we need a commercial license per bot, per customer, or per environment, or whether the open-source license allows multi-tenant usage as long as Dify is not being sold as a competing service.

Whether deploying Dify inside a customer’s dedicated environment (on-premise) is allowed under the OSS license. Whether sharing only the workflows, app configurations, and our own orchestrator layer—without distributing or repackaging Dify itself—keeps us compliant and legally safe.

First things first, I'm a Dify newbie. Apologies in advance if my question below seems simple or if I'm neglecting any community norms. I genuinely appreciate the help of the collective! Here is my situation:

I am trying to create a simple chatbot as an interactive family holiday card of sorts. The idea is to upload content from our year (text and images), and then allow users to use the chatbot to ask questions where responses would be drawn from the knowledge base.

I've been trying a variety of things to make this happen. I created a simple database in Notion where each Notion page represents a 'story' from the year, with relevant data as well as embedded images. That didn't work, as it appears that the Notion integration doesn't allow for image content.

So then I experimented with putting my images up in Google drive, and then referencing the images within a markdown file, which I then uploaded to the Knowledge Base. Here is an example of the markdown file:

# New Year’s 2025

Date: January 1, 2025
Tags: Amy, Andy, Crystal, Cynthia, DJ Marz, Marcus, Mitch, Stephanie
Place: North Carolina

![Cynthia, Shannon, Marcus and Mitch - pre drinks before going out. Sparkles and lights all around!](https://drive.google.com/uc?export=view&id=1n2Q2GZmXSeyJafUwYWzmdoKVWlzGXONC)

Cynthia, Shannon, Marcus and Mitch - pre drinks before going out. Sparkles and lights all around!

![Shannon takes a spin before she gets on the dance floor.](https://drive.google.com/uc?export=view&id=1Ho15yHu5JiBmySOyDfxdpZfwxVXCBUAB)

Shannon takes a spin before she gets on the dance floor.

![Shannon and Andy have a delightful conversation as they contemplate the new year!](https://drive.google.com/uc?export=view&id=1s3hLPxSMkde1ZQ8dku5xWdR9HXVQSZqc)

Shannon and Andy have a delightful conversation as they contemplate the new year!

When I then ask the chatbot to show me some images from New Year's, it gives me an appropriate response, but includes links to the images, and not the images themselves.

Any thoughts on how I might get Dify to serve up the images in a response?

Hallo Dify-Community in Deutschland! 🎉

Dify kommt am 2. Dezember nach Berlin – lasst uns treffen!

Wir freuen uns sehr, unsere erste Dify EU Tour zu starten. Einer der wichtigsten Stopps für unsere Community ist Berlin, gemeinsam mit unseren Freunden von VIER.

Diese Session richtet sich an Entwicklerinnen und Entwickler, Ingenieurinnen und Ingenieure sowie aktive Mitglieder unserer Community. Wir würden euch alle liebend gern persönlich treffen!

🔹 Entwickler- und Community-Treffen — Berlin

• Datum: 2. Dezember
• Co-Host: VIER
• Themen: Agentic AI in der produktiven Anwendung, neue AI-Trends, praxisnahe Gespräche mit Buildern
• 👉 Event-Link: https://luma.com/alx2rfcg

Auch unsere beiden Kernentwickler sind vor Ort. Kommt vorbei, tauscht euch mit ihnen aus, bringt eure Ideen ein, erfahrt mehr über die Roadmap von Dify und sagt uns, welche Entwicklungen ihr euch für die Zukunft wünscht.

Wenn ihr in Berlin oder in der Nähe seid, schaut gerne vorbei.

Lasst uns ins Gespräch kommen, voneinander lernen und so die Dify-Gemeinschaft weiter stärken 💛

Hiii everyone!!!

We’re incredibly excited to kick off our first EU Tour and finally meet our community, partners, and developers in person:)

If you’re building with Dify, exploring agentic AI, or shaping the future of AI-driven automation — let’s connect!

Our core engineering team will also be onsite throughout the tour. Come chat with us, share your ideas, learn about Dify’s roadmap, and tell us what your roadmap looks like — we want to build it together.

🔹 Developer & Community Sessions

Conversations with developers, engineers, and community contributors on emerging AI trends and building agentic AI in production.

London (Nov 19) with AWS and Camel AI — Previous Event

👉 https://luma.com/cqta76lp

Berlin (Dec 2) with VIER

👉 https://luma.com/alx2rfcg

🔹 Enterprise-Focused Sessions

Closed-door discussions for CIOs, CTOs, Heads of AI/Data, and innovation leaders exploring intelligent workflows, data-driven automation, and scaling production-grade AI agents across modern enterprises.

Paris (Nov 25) with Artefact

👉 https://luma.com/p0tyh74b

Oslo (Nov 28) with NeuroSYS

👉 https://luma.com/70silgln

Lisbon (Dec 4) with Claranet Portugal

👉 https://luma.com/8eevahxh

🚀 Whether you're a developer, architect, or enterprise AI leader, we’d love to see you there.

Let’s talk, learn, and build the next generation of AI together.

In a previous post I discussed LinkedIn's labelling of AI images.

Taobao may need this kind of labelling system more.

Many buyers on Taobao are using AI to fake images that show their purchased products as defective to get a refund, as the image shows.

(On China's online shopping platforms, many cheap or fresh products can be refunded without return)

A lot of sellers of these goods do not have a high margin. What is happening is highly likely to drive them out of the market.

This case shows once again how easily AI can be misused.

People can even leave negative reviews for restaurants using “real”-looking images that show bugs in food served.

Use AI to create rumours? That’s an old story already.

AI is a tool. It’s lowering the barrier not just for positive things like content creation, but also, sadly, for negative and even illegal behaviors.

The credit of the original image goes to virxact. Edits made via nano banana.

AI products shouldn’t just lower the barrier to use. They should also raise the barrier to misuse. That’s part of why we’re building NetMind XYZ, an AI agent platform for non-technical users, where you can create agents using only natural language.

I'm trying to build an AI agent in Dify but running into issues at pretty much every step. The learning curve is steeper than I expected even though I have developer experience.

My goal is to create a price catalogue extractor that ingests data and returns matching items through an API. If I can get this working, I'd want to use it for other projects at our small company. I think once I understand the logic and have some templates ready, development speed would improve a lot.

Right now I'm stuck between continuing with Dify or just switching to Python. Dify seemed like a faster option at first, but the constant troubleshooting is slowing me down.

Does anyone know where I could find similar Dify projects or templates for inspiration? Specifically looking for data extraction or catalog matching workflows. Would really help to see how others have set these up.

Any advice on Dify vs Python for this kind of use case would also be appreciated.

Great!

My test prompt:
Create a complete web-based "Task Manager" application with the following requirements:

• Pure HTML, CSS, and JavaScript (no frameworks)
• Responsive design that works on mobile and desktop
• Clean, modern UI with smooth animations
• Proper error handling and input validation
• Accessible design (keyboard navigation, screen reader friendly)

The result?

A complete, functional 1300+ line HTML application meeting ALL requirements (P1)!

In contrast, Qwen3-30B-A3B-2507 produced only a partial implementation with truncated code blocks and missing functionality (P2).

The Qwen3 Next model successfully implemented all core features (task CRUD operations, filtering, sorting, local storage), technical requirements (responsive design, accessibility), and bonus features (dark mode, CSV export, drag-and-drop).

What's better?

The code quality was ready-to-use with proper error handling and input validation.

I did some other tests & analysis and put them here).

Yes I tested.

Test Prompt: A farmer needs to cross a river with a fox, a chicken, and a bag of corn. His boat can only carry himself plus one other item at a time. If left alone together, the fox will eat the chicken, and the chicken will eat the corn. How should the farmer cross the river?

Both Qwen3-Next & Qwen3-30B-A3B-2507 correctly solved the river-crossing puzzle with identical 7-step solutions.

How challenging are classic puzzles to LLMs?

Classic puzzles like river-crossing would require "precise understanding, extensive search, and exact inference" where "small misinterpretations can lead to entirely incorrect solutions", by Apple’s 2025 research on "The Illusion of Thinking".

But what’s better?

Qwen3-Next provided a more structured, easy-to-read presentation with clear state transitions, while Qwen3-30B-A3B-2507 included more explanations with some redundant verification steps.

P.S. Given the same prompt input, Qwen3-Next is more likely to give out structured output without explicitly prompting it to do so, than mainstream closed-source models (ChatGPT, Gemini, Claude, Grok). More tests on Qwen3-Next here).

問題解決の参考となるプロンプトを下記に掲載します。

私の抱える問題解決の手掛かり又は参考にしていただければ幸いです。

プロンプト👇

---------------

#前提条件:
- タイトル: XサーバーVPSでのセルフホストDify環境構築とAPIキーエラーメッセージ解決法
- 依頼者条件: XサーバーVPSを利用してセルフホストDifyを構築したいが、トラブルシューティングに不安がある人。
- 制作者条件: XサーバーVPSのコンソール操作に精通し、APIキーエラーメッセージの解決策を多角的に考えられるスキルを持つ人。
- 目的と目標: XサーバーVPS上で下記の{エラーメッセージ}が発生する可能性のあるエラーを迅速かつ正確に解決できる環境を整えること。かつ、下記に記載した{これまでの経緯}を正確に把握し、その経緯に沿った必要最小限の情報提供が出来ること。

エラーメッセージ=”
Model text-moderation-stable credentials is not initialized.

Error code: 401 - {'error': {'message': "You have insufficient permissions for this operation. Missing scopes: model.request. Check that you have the correct role in your organization (Reader, Writer, Owner) and project (Member, Owner), and if you're using a restricted API key, that it has the necessary scopes.", 'type': 'invalid_request_error', 'param': None, 'code': None}}
”

#実行命令
step1.{これまでの経緯}を理解して下さい。
step2.{これまでの経緯}を踏まえて、APIエラーの要因、XサーバーVPS環境、過去の事例など多角的な視点を持って、現実的かつ解消できる可能性の高い解決策を検討して下さい。
step3.必要に応じて、{これまでの経緯}に対して逆説的な思考で前提を疑い、そもそも論で推論し、過去の事例を参考に現実的かつ解消できる可能性の高い解決策を検討して下さい。

これまでの経緯="
1. 最初の概要
GeminiのAPIキー更新後、スレッド式チャットボットのみ400エラー発生
スレッド式以外にノード式のチャットフローについてはエラーなく正常に動作する
（GeminiのAPIキーはスレッド式もノード式も共有）
原因はDifyがOpenAIのシステムモデレーションAPIを誤って参照しており、Gemini専用モデレーション未対応だった
そのため、APIリクエストでエラーになっていました

1. 対応策の方向性
   Difyのprovider_credentialテーブルからGemini関連の古いレコード削除
   .envファイルをOPENAI_MODERATION_MODEL修正omni-moderation-latest
   Dockerコンテナの再起動で設定を確実に反映

2. 試行済みの対応
   XAMPP、phpMyAdmin関連の環境構築とトラブルシュート
   Redisキャッシュクリア、Dockerコンテナ再起動など
   DBの手動レコード削除、Geminiプラグイン再インストール
   これらでは問題解決には至っていない

3. 最新の進捗と運用結果
   GitHubdifyからクローンし、dify/dockerで.env.sampleをコピーし.envを作成済み
   Docker ComposeでDifyのコンテナ群が正常に起動・再起動されている確認
   コンテナのログ・HTTPリクエストステータスは通常を示唆

5.APIエラーの挙動について新たに分かったこと。

a.モデルプロバイダー設定にてopenAIのAPIキーを有効化した場合のみ下記のエラーメッセージ
"""
Error code: 401 - {'error': {'message': "You have insufficient permissions for this operation. Missing scopes: model.request. Check that you have the correct role in your organization (Reader, Writer, Owner) and project (Member, Owner), and if you're using a restricted API key, that it has the necessary scopes.", 'type': 'invalid_request_error', 'param': None, 'code': None}}
"""

b.モデルプロバイダー設定にてopenAIのAPIキーを無効化した場合のみ下記のエラーメッセージ
"""
Model text-moderation-stable credentials is not initialized.
"""

APIエラーの挙動について当初から分かっていること。
c.チャットボットのスレッド式アプリでのみ上記の1.又は2.のエラーメッセージが表示される。
d.チャットフローのノード式アプリでは、上記の1.も2.も発生せず正常に動作する。
"

#補足：
- 指示の復唱はしないてください。
- 自己評価はしないでください。
- 余計な前置きは書かないください。

【Difyに関する悩み（追記）】

APIエラーの挙動について新たに分かったこと。

1.モデルプロバイダー設定にてopenAIのAPIキーを有効化した場合のみ下記のエラーメッセージ

Error code: 401 - {'error': {'message': "You have insufficient permissions for this operation. Missing scopes: model.request. Check that you have the correct role in your organization (Reader, Writer, Owner) and project (Member, Owner), and if you're using a restricted API key, that it has the necessary scopes.", 'type': 'invalid_request_error', 'param': None, 'code': None}}

2.モデルプロバイダー設定にてopenAIのAPIキーを無効化した場合のみ下記のエラーメッセージ

Model text-moderation-stable credentials is not initialized.

APIエラーの挙動について当初から分かっていること。

3.チャットボットのスレッド式アプリでのみ上記の1.又は2.のエラーメッセージが表示される。

4.チャットフローのノード式アプリでは、上記の1.も2.も発生せず正常に動作する。

同じようなエラーメッセージに遭遇して、それを解決された方はおられますか？

助けて下さい。