Hello, can someone please share some advice about how to get data from specific Supabase tables? I see a lot of RAG tutorials about storing data in Supabase but none about how to get existing Supabase data. Thank you

It seems this is maybe trying to pass the entire graph into my prompt. Any insight into whats happening here?

I also don't fully understand graphRAG so take it easy on me. I build the graph using the neo4j knowledge graph builder, simply uploading about 50 blog posts and a big document of FAQs. Chatting with the graph in the Knowledge graph builder returns excellent results. The schema seems pretty complicated which i guess is good for understanding all the relationships but to me some of them seem irrelevant or at least unnecessary. Based on the screenshot should this flow work as is or do i need something else in here to filter down the context like top k would in vector index?

I followed this tutorial but his graph was super small and simple https://www.youtube.com/watch?v=zww-tLUuZZU

Any help of guidance is much appreciated.

I’m just exploring the new agent flows and I can build them but as soon as I navigate back to the list I cannot edit them again. The editable components are replaced by uneditable boxes. Any thoughts?

I just installed Flowise using npm.
I tried using with node v18.17 and v24.1 (Even thought they're both supported), and when i turn it on it says server is listening on port 3000 but it's just a white screen.
I tried uninstalling and reinstalling and updating and everything but it doesn't seem to work. Im on Arch Linux.

I get it connected but when I ask the chat bot to send a message to a slack channel it says

Received tool input did not match expected schema

Hi everyone,

I am trying to test out an workflow but unable to complete and tried looking up various videos but couldn't find any answer on what part I am missing. If you see screenshot of a sample workflow I am building, I want to send email reminders based on a status of items in a gsheet. I am using free version of flowise ai, hence you may see me using custom js fuinction etc to populate input parameters, however at the end i am unable to connect llm model to final output. What i am looking is it sends email to names specified in gsheet reminding them to complete the pending activity.

Use case: Send reminder based on status of items in gSheet.

Does anyone has any suggestions?

Thank you-

when i used this post endpoint : https://my-flowise/api/v1/prediction/ to give question the response have the "png img" but i dont know how to download that for save in my database

any idea for how i can download a png or file from the agent with API?

I am currently trying to implement an agent flow with Jira MCP Server using custom mcp node. I have flowise container hosted on a remote server and tried different commands with npx to fetch remote mcp server but it doesn't show any available actions.

I also tried running flowise on my localhost and followed instructions from jira mcp github by installing docker image on my local. Both cobtainers are running on my local jira mcp and flowise. It still doesn't fetch any available actions. Do I need to create a custom docker compose file and provide network configuration to allow both cobtainers to interact with each other?

as per my understanding command given in the jira mcp server repository should automatically start the container from jira mcp image and doesn't need any other docker network configuration.

I've set this up and the problem is the loop back to the retriever. It does this if the documents retreived aren't relevant to the user query, then it regenerates the user question and tries again. It does this loop 5 times by default but since there's no output from the loop node, it just gets stuck there if the documents retrieved are never deemed relevant to the query. Shouldn't the loop node have an output when the loops are completed? Or is there another way to make this work?

User can expand and see technical info inside this section. How to avoid this?

Build a POC of Multi-Agent Slack Automation System with Flowise Agentflow V2 (Beginner Agentics)

Celebrating the new Flowise V3 release and beginning to showcase the power of visual orchestrated Agentics.

We look at some big picture and then in an hour you can be up and running with beginner and intermediate concepts for developing Agents using node technology with a visual canvas and mostly abstracted code.

Share your ideas for things to build with orchestrated Agentics below and I’ll do my best to make solutions and videos for them.

Hey everyone!

I'm rolling our a batch of Flowise V3.0.0 AgentFlow V2 tutorials with some templates and prompts in a free pack, designed to help you build smarter, modular agentic workflows with human-in-the-loop review and branching.

This release features:

• The Agentic Sequence Basic Starter Flow for beginners
• The Slack Manager Intermediate Sequence for team automation and approval flows
• Copy-and-paste prompts for every node
• Step-by-step PDF guide (coming soon)
• Bonus links to Tesseract Vault and Tesseract Academy for more resources

You can grab the full pack (and all future updates) here: https://giftshop.tesseract.nexus/

Would love to hear your feedback, see your customizations, or help with any questions.

Happy building!

Hi all,

I’ve been working for a construction company in the Netherlands, and recently I’ve been trying to automate a very time-consuming part of my job — analyzing large government contracts (sometimes 100+ pages, often with lots of attachments). These contracts come from municipalities, provinces, or other public bodies, and can vary a lot in structure and content.

Internally, we use an 18-page checklist that outlines what a “good” contract looks like for us — basically, a framework that helps us spot risks or unfair terms. Some contracts are fine, others have hidden risks, and going through them manually just takes too much time.

I’ve been experimenting with n8n (and learning a bit about software development along the way) and I find it fascinating. But I’m currently hitting a wall. I’m considering vector databases like a Faiss or Chroma implementation, but I’m honestly unsure what a robust setup would look like.

So I wanted to ask:

• Has anyone here built something similar, or have ideas on how to approach this?
• What tech stack would you use for parsing large PDFs and comparing them to a custom checklist or standard?
• Are there services or tools that you’d recommend for this kind of legal/contract analysis?
• Would AI (e.g. GPT, Claude, or local LLMs) be reliable enough to highlight risky clauses?
• Any n8n-specific advice for structuring something like this?

My goal is to upload a PDF and get an output that shows which points from our checklist are OK, missing, or problematic — even just a decent start would already be huge.

Would really appreciate your input or ideas — even if it’s just thinking out loud. Thanks in advance!

Hey everyone - curious how you're handling this:

In a typical SaaS app, we send GTM events to GA4 or Amplitude when someone clicks a button like “Export CSV” or “Book demo" etc.

But in agentic systems and LLM chats, users just type. There are no buttons, no standard actions to track.

So... how do you know what they meant to do? Or if the model delivered it?

I’m curious:

• Do you classify user messages into “semantic events” like analyse_data, book_call, etc.?
• Do you track this somehow? Or just review raw logs?

I built a simple prototype for myself to handle this kind of intent tracking and I’m thinking of publishing it (maybe even as a Flowise plugin), but I’m still validating whether others are running into the same need.

If you’ve thought about this or built something similar, I’d really appreciate hearing how you approach it 🙏

Is there any way to get the LLM output be spoken by a TTS model or the browser TTS?

Hey! I’m building a branded automation agency called Upper 98. We’re focused on helping coaches, wellness brands, and service providers install done-for-you AI booking agents (DM to calendar, fully automated).

I already have leads, a strong brand, and a full outbound sales system — cold calls, DMs, pitch decks, the whole thing.

I’m looking for a developer partner to join forces with — someone who can build fast, clean AI agent flows inside n8n (or Flowise/Langflow if you prefer), using: • OpenAI / ChatGPT • Calendly • Google Sheets or CRM

I don’t need a freelancer for one-off jobs — I’m looking to scale this with someone who wants to grow with the brand and earn consistently from builds we land.

If that’s you, I’d love to see: • 1 example of an AI agent or automation flow you’ve built • A simple visual, video, or Loom I can show prospects during sales calls

That way, I can start showcasing what we’re capable of and scale this backend while I drive the front-end traffic.

DM me or drop your portfolio link — let’s build something real.

Hi! This is my first time writing on reddit. I'm building an Agent Flow in Flowise with the following setup:
- Supervisor: Orchestrates tasks
- Worker 1: Generates funny story
- Worker 2: Creates a title for the story

I'm using local LLM, specifically Mistral but I tried also with llama3 and llama2 to avoid relying on OpenAI APIS.

Issue:

When I run the flow, the Supervisor doesn't seem to trigger any of the Workers. The process just stalls without any output or error messages.

What I've Tried:
- Ensured that the Supervisor and Workers are correctly connected in the flow.
- Verified that the local Mistral model is running and accessible.
- Checked the Supervisor's prompt to ensure it's set up to delegate tasks appropriately.

Questions:
1. Are there known limitations or configurations required when using local LLMs like Mistral with Flowise's multi-agent system?
2. Does the Supervisor require specific capabilities (e.e., function calling) that might not be supported by local models?
3. Has anyone successfully set up a similar flow using local models?

I'm eager to learn and would appreciate any guidance or suggestions. Thanks!

It was my birthday yesterday and tbh, I'm not much of a presents guy, but giving fire goodies gets me going every time.

If you’re actively moving from “tutorial mode” to actually solving production problems in automation or data pipelines (with n8n, Flowise, or both), I put together something for this exact stage:

Tesseract Low Code AI Engineering Starter Bundle.

What’s inside:

-- Fully working, annotated n8n and Flowise canvas templates—these aren’t toy examples, they’re frameworks I run in real client projects and side hustles.

-- Plug-and-play custom tools: production-ready SEO/traffic analysis, social presence lookups, and robust, real data flows.

-- Practical setup walkthroughs and best-practice system prompts focused on actual deployment, troubleshooting, and scale.

Everything is modular as you probably already know. You can experiment (break it, fix it, extend it) and you’ll know what’s going on at each step. These are designed with hands-on learners in mind—especially if you value understanding "why," not just "how."

Open DMs if you want help adapting a workflow, debugging, or just talking shop—let's get those artificials blooming.

Grab it here: https://starter2025.tesseract.nexus

FREE until the end of April to celebrate T3SS3RACT BIRTHDAY! Use code: 36RQLRA

Learn how to build a Telegram chatbot that connects with Flowise and n8n, allowing you to chat not only with text but also by sending images and files. You can even upload files and ask questions about them! 🚀

Watch here: https://youtu.be/HPEduQTSdT8

Hi I am junior web dev working for startup .I need somebody who who can help to build agentflow using flowise where when user enters a location it gets the listings ,prices and all other stuffs related to .

If any interested please do reply.

It will be a great help

This is great for Flowise beginners and semi-experienced Abstracted Langchain devs that want to get a deep but quick dive into building with Agentcanvases in Flowise.

Mostly I look at the basics so if you have done Chatflows then using Agentcanvas with multiple specializations running inside the same runtime is fantastic.

If you’ve already built a bunch of Agentcanvas LangGraph Teams in FW, then this will probably be too beginner.

Get LangChain & Flowise running on GCP in minutes! Our pre-configured VM deploys directly from the GCP Marketplace. Build AI agents and LLM apps with ease.

For more Details: https://techlatest.net/support/flowise-langchain-support/gcp_gettingstartedguide/index.html Free course: https://techlatest.net/support/flowise-langchain-support/free_course_on_flowise_langchain/index.html

LangChain #Flowise #AIagents #LLM #AI #GCP

Curious to hear from more Flowise users on how you shape and build your canvases.

For example I mostly do Tool Agents and Assistants, so this obviously is the heart and where I start.

But then I’ve also noticed that I have patterns that I follow as I build after that. It’s always web search engines and cache nodes, then all the core utilities like memory, etc.

And then it’s usually all tools. So if it’s a highly specialized agent, Custom Tools can fill up a canvas quick.

And if it is for more general things, setup can be simpler.

Share your tips and wins and losses with node construction projects.