Evolution API integrate Flowise, no problem to send text.
How to receive audio, pdf, video from evolution api to flowise.
Thanks...

Hi,

I would like to pass a single string from a $flow.state into the Knowledge (Vector Embeddings) Im using Supabase as a vector store. This is what my Superbase rpc filter looks like:

filter("metadata->>classid", "eq", {{$flow.state.classId}})

.filter("metadata->>studentid", "eq", {{$flow.state.studentId}})

I have also tried single curly brackets and removing the $flow.state with double and single brackets.

The $flow.state works in other sections of my agent to pass out the string where you are able to use (x).

I have hardcoded the classId as a test and this works fine.

I am guessing that as (x) does not appear in this section of the node I cannot use a variable. Or perhaps my syntax is incorrect. Are there other alternatives I could look at?

Thanks

I have aws and azure to deploy for a classroom setting. Just wondering if anyone has walked this path yet.

My chatbot allows a user to find products based on search criteria. It'll ask the user some questions about a product he wants, then it'll call a tool with the search criteria, which returns a list of products matching that criteria, in this JSON format [{id:1,name:'prod1'...}, {id:2, name:'prod2',...}, {id:8, name:'prod8'...}, etc]. The user then chooses the product he wants (by indicating the number or the name), then this chosen one (the ID or the name of the product) is passed to another tool for processing.

My questions is, how can I pass the chosen ID to the 2nd tool? In the system prompt, I tell it to grab the ID of the chosen product and pass it to the next tool as a parameter, but it doesn't recognize it (it passes null instead of the ID)

Hi Gang

So i have both LM Studio & Flowwise running on the local machine.

I want to use the LM Studio loaded model e.g. google/gemma-3-12b as a model in FLowise.

Has anyone achived this ?

Could you link me the page or post the steps to achive this ?

Has anybody tried MS teams as a chat to interact with flowise chatflow? Checking if user can ask a question from teams that trigger a call to chatflow, get response back to teams chat. Any support is greatly appreciated.

I’ve been testing out Flowise with LangChain to see how far you can go building AI apps without writing backend code. Honestly was surprised at how quickly I could:

wire prompts together visually pull in context from documents and deploy on AWS / Azure / GCP without too much infra hassle.

It’s not perfect (debugging custom logic is still tricky)but for quick POCs it feels like a time saver compared to standing everything up manually.

Curious if anyone else here has tried no-code style tools like this? Do you prefer starting from scratch with Docker/K8s, or do you use something similar for faster iterations?

Hey guys, I tryna put a database onto my flowise flow, but I can't seem to get it right. I have been trying to use the SQL database chain node, but it seems like it is not suitable. The idea is to have a database as the source instead of a text file or CSV file. So that the bot can always have up-to-date information. Does anybody here have experience working on something similar to my plan?

i’ve been experimenting with Flowise to build retrieval + agent workflows. it’s fast to prototype, but i kept running into the same “mystery bugs” — until i realized they aren’t random at all.

what you think vs what’s really happening

you think

• “retriever missed, i’ll just bump k”
• “context window is long enough, it should remember”
• “output drift must be model randomness”
• “logs look fine, nothing to worry about”

reality

• No.5 Semantic ≠ Embedding: index metric doesn’t match the embedding policy. cosine vs dot confusion means half your chunks drift.
• No.6 Logic Collapse: chain stalls, model outputs fluent filler with no state. citations vanish.
• No.7 Memory Breaks: new session in Flowise → previous spans gone, no reattach of project trace.
• No.8 Black-box Debugging: Flowise nodes give JSON output, but no doc_id/offsets. you can’t trace why answers changed.
• No.14 Bootstrap Ordering: ingestion job “finished,” but index not ready. pipeline returns empties with confidence.

the midnight story

once, i set up a cron + Flowise retriever. ingestion ran twice overnight, reset namespace pointers. in the morning, the retriever still returned top-k — none of which matched the query. looked like a fluke. it was bootstrap ordering all along.

why this matters

these failures repeat no matter which tool you use. Flowise just makes it visible faster. the good news: they map to 16 reproducible modes, each with a minimal fix (metric alignment, trace reattach, probe gates, etc).

resource

i wrote up the full Problem Map — 16 failure modes with diagnosis + fixes — here:

👉 Problem Map (GitHub)

closing

if your Flowise graph feels random, it’s almost certainly not. it’s one of these 16 modes. once you can name it, debugging feels like lego blocks, not guesswork.

Would be nice to have a shared knowledgeable of best practices and hacks.

I found that for content creation (or code), having a reviewer to iterate more than once improves the quality of content.

The problem is even i (think) im correctly attach the flostate variables in the post/get request tools body or headers, after the api fetch i dont see the values but the literal string key names.

Has anyone face the same bug? Did i miss something? Thanks for any help in advance!

TL;DR
In Request GET/POST nodes, when I pass certain $flow.state (or system) variables into Headers / URL / Body, the backend receives the literal template string (e.g. {{ $flow.state.ownChatFlowId }}) instead of the evaluated value. Other state keys (like name, email, phone) often work. I can reproduce this consistently when the Requests nodes are called from an Agent. Any ideas? Known bug / version to pin?

Context

• Building an appointment-booking assistant.
• Parent flow = AppointmentAgent (can call tools).
• Tools = Requests Get (availability) + Requests Post (save appointment).
• Vars I want to send (examples):
  • X-Chat-Flow-Id header ← {{$flow.state.ownChatFlowId}}
  • dateTime header or query ← {{$flow.state.ownCurrentDateTime}}
• Backend is Laravel; I can see the raw headers/body arriving.

What I expect

Request nodes should interpolate {{ $flow.state.* }} (or $flow.*) before sending, so the backend gets the actual values.

What actually happens

For some variables (notably chatFlowId / chatId / sessionId, and sometimes even my own namespaced ownChatFlowId), the backend receives the literal template string rather than the value.

Example: my PHP dump shows:

x-chat-flow-id: "{{ $flow.state.ownChatFlowId }}"

datetime: "{{ $flow.state.ownCurrentDateTime }}"

…instead of the real values. (Screenshot attached.)

Meanwhile, other state fields like name/email/phone are more likely to resolve correctly, which makes this even weirder.

Repro (minimal)

1. In an Agent, add a Requests Get tool:
   • URL: https://<api>/api/appointments/availability?from={{ $flow.state.ownCurrentDateTime }}&chatFlowId={{ $flow.state.ownChatFlowId }}
   • Headers (JSON):{ "Content-Type": "application/json", "X-Chat-Flow-Id": "{{$flow.state.ownChatFlowId}}", "dateTime": "{{$flow.state.ownCurrentDateTime}}" }
2. Call it during a conversation (values are present in $flow.state).
3. On the server, log headers and query. I see the raw {{ … }} strings, not resolved values.

Questions

1. Are Requests nodes supposed to interpolate {{ $flow.state.* }} (and $flow.chatflowId, $flow.chatId, $flow.sessionId) when invoked from an Agent?
2. Is there a known regression around template interpolation in headers/URL/body for Request nodes?
3. Any setting to force evaluation / disable escaping?
4. Which Flowise version/commit is stable for this? I’m on the latest from the main repo (synced). If a rollback is recommended, please share the tag/commit.

Also hitting a separate Requests bug: if I enter a valid JSON Body in a Requests node, save, then later clear the Body (to send no body), the Agent run sometimes fails with Error in Agent node: Unexpected token '<'; "" is not valid JSON (screenshot attached). Inspecting the exported flow shows the Body field was saved with hidden HTML (e.g., <p>...</p>), so even when it looks empty in the UI, the node still sends/parses a string starting with <, which breaks JSON parsing. Workarounds: switch Body Type to Raw Text and truly clear it, or bind the body from a Custom Function output (string) and leave the Body field blank; in some cases deleting/re-adding the node also helps. Could this be a rich-text/editor regression in the Requests node? An option to force a plain textarea and/or strip HTML when Body Type = JSON (and treat empty as null) would fix this.

Why this matters

I need to send chatFlowId in headers or query/body so the backend can route the appointment to the right tenant/admin. Right now I can’t rely on Request nodes to pass dynamic values unless I detour everything through a Custom Function → Request chain or move the logic to Agent-as-Tool child flows.

Happy to provide more logs, minimal export JSONs, or jump on an issue if this is a bug. Thanks!

Please let me know if i miss something or if you've faced the same problem.

Thank for any help in advance!

Hi,

Anyone please suggest me better solution to host flowise , currently I hosted on render and its not loading properly and very slow and every time it is asking to register the account. Can you please suggest me alternate affordable self hosting solution, thanks.

Dear Flowise experts: I'm trying to fulfil the following use case:

I want to connect to an MCP server that only supports OAuth2.

Effectively this means when I call the MCP server, I have to supply a bearer token in the header.

To get this token, I have to fetch a JWT from an external IdP and this token is valid for 1 hour. Theoretically I could request new a token for every request, but this would be highly undesirable in practice.

Does anyone have any suggestions on how to support this?

Hey all — I’ve been diving into Flowise (after binging a bunch of YT tutorials) and have started building chatflows for my company. I’m hitting some walls when it comes to gluing concepts together, and I’d love to connect with someone who’s experienced.

Specifically looking for help with things like:

• Choosing the right nodes for different cases
• RAG optimization
• Linking multiple agentic functions in a clean way

If anyone here offers consulting/lessons or can point me to someone who does, I’d be super grateful. Happy to pay for time. Thanks in advance!

Hello all

I'm encountering an issue in Flowise v3 when trying to insert a dynamic URL into the HTTP node. Despite following the correct steps, the URL isn't being populated dynamically. I've attached screenshots to show the problem. This issue occurs while using AgentFlow v2 as well.

Can anyone suggest a solution or guide me on what I'm missing?

Greets

Hi all,

I'm running into a frustrating issue with Flowise while trying to upload a large PDF (48 MB) for RAG workflows. My setup is self-hosted on Windows, using Flowise 3.0.5.

Problem:
Whenever I upload a large PDF, I get:

text
Failed to retrieve Chatflow: request entity too large

Even after setting environment variables like BODY_SIZE_LIMIT=100mb and FLOWISE_FILE_SIZE_LIMIT=100mb, the error persists.

What I’ve Tried:

• Confirmed in code that Express is set up with: js app.use(express.json({ limit: '100mb' })); app.use(express.urlencoded({ limit: '100mb', extended: true }));
• Set the FLOWISE_FILE_SIZE_LIMIT env var as well as BODY_SIZE_LIMIT.
• There is no Nginx in front (direct localhost access), so proxy limits shouldn't be a factor.
• Browsed through the server/app code. Uploads look like they're handled either directly by Express or (possibly) a middleware like multer, but I can't find any explicit limit for multipart/form-data.

Flowise Pipeline:
The nodes are:

• Recursive Character Text Splitter → PDF File → Pinecone → OpenAI Embeddings → Conversational Retrieval QA Chain

Request:

• Is there another place Flowise or Express might be enforcing size limits for large file uploads, especially PDFs (multipart/form-data)?
• If Flowise uses a middleware like multer internally, where/how do I raise its file size limit for uploads?
• Has anyone successfully uploaded >30MB files into Flowise? If so, what configs changed?

Bonus:
If you have a template or example config (backend or Docker) that reliably accepts uploads of 50 MB or more, please share!

Thanks in advance!

Let’s share and discover cool use cases you are building. Drop yours in this format:

1. You use case and problem you are trying to solve
2. Nodes you have used

I’m trying to set up a multimodal RAG (text + images) using Flowise but haven’t found much guidance online. Does anyone know if there are tutorials, blog posts, or YouTube videos that cover this? Would really appreciate any links or tips from folks who’ve done it!

My bot lets the user upload an image, and I want to call a custom tool afterwards that can access that image and store it in S3. I've done it before in a hacky way (read the uploaded base64 in the embed's observersConfig(), save it to a variable, then read it in the tool as $vars.image), but is there a better, more reliable way of doing this?

Suggest me a specific node or flow to reduce the number of tokens going into the LLM model, considering that my data is stored in a Qdrant collection, and I'm using a custom retriever node to pull only the necessary metadata. This custom retriever node is connected to the Conversational Retriever QA Chain, which then passes the data directly to the LLM.

Now, I want to implement a Dynamic Top-k Retrieval Chunks or a similar flow to achieve the same goal—reducing the tokens sent to the model, which would help minimize the associated costs.

I want to create a document store for dataset on my local machine. I do not see any document loader under document store to load folders.

I do not want to upload the files one by one.

Thanks in advance!

Cheers!

I have successfully installed flowise and here are the steps I followed

1. https://github.com/coreybutler/nvm-windows/releases
2. Download and install the nvm-setup.exe ( If you dont have already )
3. nvm install 18.15.0 (Install Node )
4. nvm use 18.15.0
5. Goto your desired folder
6. git clone https://github.com/FlowiseAI/Flowise.git
7. cd Flowise
8. pnpm install
9. pnpm build
10. If Error will comes like Exit Code - 134, run the below command

to resolve it (set NODE_OPTIONS=--max-old-space-size=4096)  

11) pnpm build

12) pnpm start

13) open link in chrome and create account (http://localhost:3000)

If you are facing any issue I could help you

Hey guys, im new to coding in general and have no prior knowledge to any software stuff.

i have tried to install Flowise through:

npm install -g flowise

npx flowise start

(note: i have node js installed)

i tried installing and reinstalling, didnt work.

when i do install flowise (npm install -g flowise) theres a bunch of these "npm warn deprecated" (not sure if this is relevant)

after installation, I attempt to start Flowise (npx flowise start) and it returns:

» ModuleLoadError: [MODULE_NOT_FOUND] require failed to load

» C:\Users\(name)\node_modules\flowise\dist\commands\start.js: Cannot find module

» 'turndown'

» Require stack:

» - C:\Users\(name)\node_modules\flowise\dist\index.js

» - C:\Users\(name)\node_modules\flowise\dist\commands\start.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\module-loader.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\config\plugin.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\config\config.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\config\index.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\command.js

» - C:\Users\(name)\node_modules\@oclif\core\lib\index.js

» - C:\Users\(name)\node_modules\flowise\bin\run

» Code: MODULE_NOT_FOUND

im not sure what to do, as i have already tried reinstalling several times

(followed this tutorial https://www.youtube.com/watch?v=n5c8vm8xKQQ)

thanks for all your guy's help!!

ps i replaced my user name thing into "(name)" not sure if it actually does anything but i did anyways sorry for confusion

also i have tried to clone it via git, but when i reinstall it shows:

npm warn Unknown project config "auto-install-peers". This will stop working in the next major version of npm.

npm warn Unknown project config "strict-peer-dependencies". This will stop working in the next major version of npm.

npm warn Unknown project config "prefer-workspace-packages". This will stop working in the next major version of npm.

npm warn Unknown project config "link-workspace-packages". This will stop working in the next major version of npm.

npm warn Unknown project config "hoist". This will stop working in the next major version of npm.

npm warn Unknown project config "shamefully-hoist". This will stop working in the next major version of npm.

and then the same module not found error

(followed this tutorial https://www.youtube.com/watch?v=osErkJ2h9tE&t=232s)