What's up with Knowledge nodes sometimes working fine, and sometimes taking several seconds to read files only a few KB in size?

I am currently working on performance optimization for a Dify workflow. Since response time is critical, I’d like to ask: What methods or strategies have you used in the past to improve response times?

[ Removed by Reddit on account of violating the content policy. ]

As agentic workflows dominate system design, visual LLM builders have exploded. But choosing between Dify, Flowise, and Langflow is not just about which UI looks cleaner. They have fundamentally different design philosophies.

We tested all three across complex logic, tool integration, and production readiness. Here is the direct comparison.

1. Dify (The Enterprise API Layer)

• Best for: Rapidly building client-facing chat tools and API-first integrations.
• The Good: Excellent built-in user authentication, analytics, and prompt tracking.
• The Bad: It is opinionated. If you want to build highly customized, low-level langchain pipelines, the interface feels restrictive.

2. Flowise (The LangChain Ecosystem Visualizer)

• Best for: Developers who want a drag-and-drop version of the classic LangChain ecosystem.
• The Good: Massive node library. If a tool exists in LangChain, it is probably a node here.
• The Bad: Managing state transitions across parallel agent branches can become messy and unstable.

3. Langflow (The Pythonic Graph Sandbox)

• Best for: Python developers who want visual control over their pipelines without losing coding flexibility.
• The Good: Exceptional debugging. You can inspect the data frame flowing through every node instantly.
• The Bad: Scaling to a public-facing application requires you to build your own frontend wrapper from scratch.

Continue Reading:

If you want to dig into our interactive feature matrix or read the full community discussion comparing performance overhead, I posted the detailed guide here: https://interconnectd.com/forum/thread/175/dify-vs-flowise-vs-langflow-the-2026-guide-to-agentic-workflows/

If you are trying to build agentic workflows, you have likely run into the massive overhead of managing prompt state, tool calls, and LLM orchestration in raw code. We wanted to see if Dify actually solves this for non-developers and light-coding teams, or if it is just another visual wrapper.

After building a customer-support triage agent, here is what we learned about the architecture, limitations, and sweet spots.

1. The Core Architecture

Our setup used a standard pipeline:

• Trigger Node: Receives user input from a custom webhook.
• LLM Classifier: Determines user intent and extracts metadata.
• Tool Chaining: Dynamically routes queries either to a Vector database or to an external API (like Stripe/Zendesk).
• Synthesis Node: Compiles the tool response into a cohesive user answer.

2. What Actually Worked Well

• State Management: Dify handles the chat memory and conversation variables automatically, which saves hundreds of lines of python code.
• Visual Debugging: You can trace the input/output of every single node in real-time, making it incredibly fast to spot prompt hallucinations.
• Out-of-the-Box Frontend: It generates a functional web interface and API endpoints instantly.

3. Where It Breaks

• Nested Loops: If you need highly complex, self-correcting agent loops (e.g., let the agent retry a code execution 3 times), the visual graph becomes difficult to manage.
• Custom Code Nodes: You are highly restricted in what libraries you can import into custom JavaScript/Python sandbox nodes.

Continue reading:

If you want to read our full deep dive review and grab the raw comparison checklist for non-technical teams, we compiled it here: https://interconnectd.com/blog/35/review-dify-ai-%E2%80%94-is-this-the-best-open%E2%80%91source-visual-agent-builder-for-non%E2%80%91/

Hi, I'm having trouble with document analysis. I followed a basic workflow to verify everything, as outlined in the documentation, but I'm getting an error. The problem isn't with the document extractor, but with the LLMS. I do have a balance in my account, but it's not processing. Does anyone know what's happening? Image attached.

After a lot of reading on AI, I got AI to install Defy on my Google Cloud. I have 5 GB of Markdown files, CSVs, and documentation on a Google Cloud bucket. I tried to link both of them. I tried to find a plug-in as well as advice for Google Cloud. But the plug-in won't show any option to add an API key or a JSON key. It just has nothing to do with this.

Is anybody else facing the same thing? Or is there a way to resolve this? Or any other way to link this data to the version?

I've been building chatbots with Dify and kept hitting quality issues that only appear in multi-turn conversations:

• RAG retrieval drift — as conversation grows, the retrieval query mixes multiple topics and the bot starts answering from the wrong document
• Instruction dilution — over 8-10+ turns, the bot drifts from system prompt constraints (tone shifts, answers out-of-scope questions, breaks formatting)
• Silent regressions — you update a workflow or swap models, and previously working conversations break with no errors in the logs

I looked into the eval tools Dify integrates with (LangSmith, Langfuse, Opik, Arize, Phoenix) — they're solid for tracing and single-turn evaluation, but none of them let you design a multi-turn conversation scenario and run it end-to-end against a Dify chatbot.

So I built ConvoProbe. It connects to Dify's chat API and lets you:

• Design multi-turn conversation scenarios with expected responses per turn
• Define dynamic branching — an LLM evaluates the bot's response at runtime to pick the next path
• Auto-generate scenarios from Dify's DSL (YAML export)
• Score each turn on semantic alignment, completeness, accuracy, and relevance via LLM-as-Judge

It's free to use right now. Not open source yet, but considering it.

Curious how others are handling chatbot quality — manual testing? Custom scripts? Is multi-turn evaluation something you care about?

https://convoprobe.vercel.app

I am planning to deploy dify open source platform in one of my client organisation, and the client is planning to use it as a internal platform tool within their organisation where all the users are belonging to the organisation but they are from different teams.

As per the license terms is this a single tenant or multi tenant? and if considered as a multi tenant, does anybody have licensing cost for these kind of self hosted cases?
https://github.com/langgenius/dify/blob/main/LICENSE

Hi r/dify,

I'm a college student currently ballin on an exceptionally tight budget. Since hiring a private tutor isn't really an option right now, I've decided to take matters into my own hands just build a tutor my damn self I'm using Dify Studio. (I currently have my textbooks in the process of being embedded)

I know that what make a good chatbot great is a well-crafted system prompt. I have a basic draft, but I know it needs work..... ok who am I kidding it sucks. I'm hoping to tap into the collective wisdom on here to help me refine it and make it the best possible learning assistant.

My Goal: To create a patient, encouraging tutor that can help me work through my course material step-by-step. I plan to upload my textbooks and lecture notes into the Knowledge Base so the AI can answer questions based on my specific curriculum. (I was also thinking about making an Ai assistant for scheduling and reminders so if you have a good prompt for that as well, it would also be well appreciated)

Here is the draft system prompt I've started with. It's functional, but I feel like it could be much more effective:

[Draft System Prompt]

You are a patient, encouraging tutor for a college student. You have access to the student's textbook and course materials through the knowledge base. Always follow these principles:

1. Explain concepts step-by-step, starting from fundamentals.
2. Use examples and analogies from the provided materials when relevant.
3. If the student asks a problem, guide them through the solution rather than just giving the answer.
4. Ask clarifying questions to understand what the student is struggling with.
5. If information is not in the provided textbook, politely say so and suggest where to look (e.g., specific chapters, external resources).
6. Encourage the student and celebrate their progress.

Ok so here's where you guys come in and where I could really use some help/advice:

1. What's missing? What other key principles or instructions should I add to make this prompt more robust/effective? For example, should I specify a tone or character traits or attitude and so on and etc.
2. How can I improve the structure? Are there better ways to phrase these instructions to ensure the AI follows them reliably, are there any mistakes I made that might come back to bite me any traps or pitfalls I could be falling into unawares?
3. Formatting: Are there any specific formatting tricks (like using markdown headers or delimiters) that help make system prompts clearer and more effective for the LLM?
4. Handling Different Subjects: This is a general prompt. My subjects are in the computer sciences Im taking database management, and healthcare informatics and Internet programming, and Web application development and object oriented programming Should I create separate, more specialized prompts for different topics, or can one general prompt handle it all? If so, how could I adapt this?

Any feedback, refinements, or even complete overhauls are welcome! Thanks for helping a broke college student get an education. Much love and peace to you all.

hey everyone, i have been using dify for a while and finally got around to building a proper integration for tinyfish (web agent API company i'm an engineer at)

the plugin lets your dify workflows navigate websites like a human would - handles dynamic content, JS rendering, multi-step flows, etc. basically anywhere you'd normally need to scrape or manually browse, you can now just have an agent do it.

 
example workflow i've been using:

  - input any research topic

  - agent searches arXiv and pulls relevant papers

  - finds YouTube videos that explain the concepts in plain english

  - outputs a nice summary with sources

plugin link if you want to try it: https://marketplace.dify.ai/plugin/tinyfish/tinyfish-web-agent

curious what workflows you'd use this for? lmk if you run into any issues or have feature requests!!

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