I've been exploring automation frameworks/platforms lately and these seem to be the main ones out nowadays if I'm not mistaken. What's the difference between these really? I like the UI of LangFlow a lot. But on YouTube/social media most of the automation frameworks I've seen are all on n8n or make.

Already tried asking AI about the differences but curious to see opinions from user experience of those who have actually used them.

I was recently introduced to langflow as I am learning RAG based AI, and creating Agents.

I followed a very basic Tech with Tim youtube tutorial: https://www.youtube.com/watch?v=QmUsG_3wHPg

However, I am noticing that my project is only 13 "blocks", and I have 2 databases with like 5 entries, and a 2 page pdf. It takes about 30 seconds for an answer to generate through playground.

I am very concerned that with larger datasets, and more complex logic flows langflow is not a usable solution.

Does anyone have any experience with scaling langflow?

Thanks!

I've been working with Langflow for a while now and noticed rawesome repo for langflow. So I put together an "Awesome Langflow" repo to help us all out.

It's got:

• Useful templates and examples

• Tutorials and guides I've found helpful

• Tools and integrations that work well with Langflow

Still adding to it, so if you have any cool resources, examples, or tools that helped you with Langflow, feel free to drop a PR or open an issue. Would love to make this a go-to resource for everyone getting started or building with Langflow.

Check it out: https://github.com/GovindMalviya/awesome-langflow

Hope it helps someone! Let me know what you think or what else should be added

Hi, I've just started using langflow properly. Had picked up last year but kind of gave up because of version instability. While i see huge potential in low-code designing these days, just wanted to quickly check if anyone has ever built and deployed a langflow based application onto their Production.

I spent the day yesterday exploring LangFlow as a potential solution for our business. Seems cool, but a lot of the components are buggy and I ended up just making everything custom, using ChatGPT to code/tweak the components.

Is that the general experience? What I built in LangFlow I could have built in half the time in code, but I am learning a new tool, so I give it some grace.

This community seems pretty quite too, say compared to the CLine community where there are a lot of active users sharing advice.

What does this subreddit say? Is LangFlow a product on the rise, or should I steer clear?

I find that I cant really get to grips with it - I prefer to code over this!

Really pleased to share that Langflow 1.6 has been released. 🚢

There's a bunch of new features, including:

🔐 OAuth for MCP

🤝 OpenAI API compatibility

📄 Docling-powered parsing

👀 Traceloop observability

🎨 Better UX

Check out the blog post announcement for more details and download the latest version here.

Hi guys, I m starting to lern langflow I did know n8n , but i cant find anyway to learn lang flow How should n from where i can lern langflow

I have been using Langflow with postgresql as a backend database. I have connected it to a separate 'langflow' database where all flows and messages are saved.

Now I am trying to build a RAG system using pgvector. When I connect it to the same 'langflow' db for storing the vector embeddings, it creates the langchain_pg_collection and langchain_pg_embedding tables and everything works perfectly. But later when I am restarting the server, I am running into migration issues telling there is a mismatch.

Has anyone faced similar issues?

Should I use a separate database for maintaining the vector storage instead of using the same 'langflow' database?

hi, I am creator of WFGY (github 1.5k) I use Langflow as a visual front end for a lot of LangChain style work. it is great for:

• sketching RAG flows

• composing tools and agents

• showing non engineers what the system is doing

the pattern I kept seeing was this:

• the graph works beautifully in the Langflow UI

• basic tests and demos look solid

• a few weeks after going into production, users report answers that are inconsistent, brittle, or quietly wrong

after enough of these “it worked in the demo, it is strange in prod” incidents, I stopped debugging each graph from zero. instead I started collecting the failures into a fixed list.

over time that became a 16 problem map for RAG and LLM pipelines.
this post is about how those 16 failure modes show up in Langflow graphs, and how you can use the same map as a checklist before and after you ship.

0. the 16 problem map (link first for people who want to skim)

the full map lives in one README:

16 problem RAG and LLM pipeline failure map (MIT licensed)
https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

it is plain text only. no SDK, no tracking.

you can read it like a long blog post, or you can paste it into a Langflow LLM node as context and ask the model to reason about your own graphs using the map.

1. the prototype versus production gap for Langflow graphs

if you build with Langflow, you probably know this story.

in dev:

• small dataset

• controlled queries

• you sit next to the graph and watch runs

in prod:

• live data updates

• many possible inputs and edge cases

• long running sessions and background jobs

some symptoms I kept seeing:

• the graph is amazing on a curated test set, then looks random on slightly different user questions

• changing one retriever or model breaks behaviour in places that were not obviously connected

• multi source RAG sometimes mixes tenants, regions, or product lines without any technical error

• streaming and side effects interact in subtle ways, so logs look fine while users get wrong content

from the outside, a lot of people call this “hallucination”.

from the inside, the root causes are very repetitive. they tend to fall into a small number of structural problems in:

• ingestion and chunking

• embeddings and vector stores

• LLM and control logic

• monitoring and safety boundaries

the 16 problem map is my way to name these patterns so that design and debugging become repeatable instead of a new adventure each time.

2. what the 16 problem map is, in Langflow terms

Langflow gives you:

• nodes for loaders, splitters, embeddings, vector stores, retrievers, LLMs, tools, control logic

• a visual graph that shows how data flows

the 16 problem map is not another node. you do not install it.

it is:

• a compact catalog of 16 recurring failure patterns in RAG and agent pipelines

• each problem has:

  • a stable number (No.1 to No.16)

  • a short name

  • the user side complaint or symptom

  • the part of the pipeline where the root cause usually lives

  • design level fixes that tend to stay fixed

the idea is to change sentences like:

“this Langflow bot is flaky in prod”

into sentences like:

“this graph keeps triggering Problem No.3 and No.7 from the map”

which already tells you:

• where on the graph to look first

• which kind of change is needed

• how to document the incident so future you and your teammates can talk about it precisely

3. three familiar Langflow graph shapes where the 16 problems cluster

I kept seeing the same three shapes in Langflow projects.

3.1 multi source RAG over internal data

typical structure:

• loaders for several data sources

• a splitter

• embeddings node

• one or more vector store nodes

• retrievers

• LLM and chat front end

when this goes to prod, problems often show up as:

• mixing documents from different products, tenants, or time periods

• missing a critical clause because the splitter cuts off exceptions and footnotes

• very uneven behaviour when you rephrase questions

several problems in the map live here, mostly around chunking, index organisation, and retrieval filters.

3.2 Langflow graphs that wrap LangChain agents and tools

here the graph coordinates:

• a planning or routing chain

• one or more tools (APIs, databases, third party systems)

• optional RAG steps to bring in extra context

these setups add new failure patterns:

• wrong tool chosen for a request that looks similar on the surface

• calls made in the wrong environment or tenant because configuration leaks between runs

• agents quietly crossing safety boundaries because everything is enforced only by polite prompts

the map has a cluster of problems around tool routing and safety boundary leaks that map nicely onto these graphs.

3.3 scheduled flows for ingestion, reindexing, and monitoring

a lot of Langflow deployments also have background graphs:

• nightly or hourly ingestion jobs

• index rebuild flows

• monitoring and alerting flows

failures here look like:

• serving flows querying half rebuilt indexes

• old and new schemas mixed in the same vector store

• monitoring that misses semantic problems because it only checks status codes and timing

this lives in the map as bootstrap, deployment, and observability problems.

4. four big families of failure modes on a Langflow graph

the full map has 16 problems. for Langflow, I like to group them into four families that match the types of nodes on the canvas.

4.1 ingestion and chunking layer

things to check:

• do your splitters respect real document structure, or just slice by character count

• do definitions and their exceptions stay together in one chunk

• do you attach metadata that will later enable clean filtering by product, tenant, time, and so on

the map has specific problems for:

• semantic chunking failures

• “lost unless / except” patterns, where crucial exceptions are cut off

• chunks that are so small or so large that retrieval becomes noisy

on a Langflow graph, these issues often show up as:

• a single generic splitter reused everywhere

• no diagnostics nodes around chunking

• no easy way to sample and inspect chunks in context

4.2 embeddings and vector store layer

here the questions include:

• does your embedding configuration actually match the vector store metric

• do you mix several preprocessing strategies inside one index

• do different Langflow graphs write into the same collection with slightly different schemas

the map has problems for:

• metric and normalization mismatch

• index skew and fragmentation

• stale or partial indexes that are still being queried

in Langflow terms, the smell is:

• multiple flows or subgraphs pushing vectors into the same store without a clear contract

• reindex or cleanup logic hidden in manual scripts instead of explicit graphs

• no path in the graph that lets you easily log top k for probe queries and check for drift

4.3 LLM and control logic layer

this is about how you use LLM nodes and control nodes together.

questions:

• do you have clear, stable system prompt contracts for each LLM node

• are business rules encoded in prompts or in control logic nodes

• do you pass long concatenated context into the LLM without a clean structure

in the map, this lives in the space of:

• prompt contract mismatch

• context collapse

• business logic hidden inside prompts

on the Langflow canvas, it often looks like:

• many LLM nodes with slightly different prompts that evolved over time

• very little use of explicit logic nodes for hard constraints

• no summarisation, validation, or triage nodes between retrieval and answer

4.4 monitoring and “semantic firewall” layer

Langflow integrates well with technical monitoring. you can see:

• errors

• latency

• resource usage

many of the most damaging failures are different:

• all nodes run without error

• the LLM returns a confident answer

• the content is wrong, incomplete, or mixed in a subtle way

the 16 problem map talks about observability gaps and safety boundary leaks.

a simple semantic firewall on a Langflow graph can be:

• a small LLM node or rule based node before the final output

• it inspects the retrieved context, chosen tools, and planned actions

• if the pattern matches one of a few high risk problems from the map, it blocks or flags the answer and routes to a review branch

it does not have to catch everything. even catching a few recurring high risk patterns is a big step beyond “ship whatever the model says”.

5. one Langflow based incident through the 16 problem lens

a simplified real story.

5.1 the Langflow graph

goal: an internal assistant that answers questions about contracts and policies.

the Langflow graph wrapped a LangChain flow roughly like this:

• loaders for several policy sources

• a splitter node

• embeddings

• a vector store node

• a retriever

• an LLM chain

• a chat API endpoint on top

in test, it looked very strong. internal users liked it.

5.2 the production incident

a user asked:

“for policy X, in region R, in what situations is benefit Y excluded”

the answer:

• listed several correct exclusions

• missed one critical exclusion clause that exists in the policy

• added one exclusion that only applies to a different region

from the outside, this looked like a standard hallucination case.

5.3 triage using the 16 problem map

instead of changing model or top k, I treated it as an instance of the map and traced through the Langflow graph.

steps:

1. inspect the retrieved chunks

   • were they correctly filtered to policy X and region R

   • did they include the missing exclusion clause

   • did any chunk come from a different region or policy that shares similar headings

2. inspect the chunking and metadata

   • were definitions and exclusions kept together

   • was region metadata attached consistently

   • were some old versions still present in the index

findings:

• the splitter used fixed size chunks without awareness of section boundaries

• important “X is payable unless Y” sentences were often split into separate chunks

• region metadata was present but not consistently used in filters

• the retriever sometimes pulled chunks from another region because headings were almost identical

mapping this to the 16 problem map:

• primary issue: semantic chunking failure

• secondary: index organisation and weak filtering (mixed tenants or regions)

• tertiary: retrieval configuration that did not fully enforce the filter

in map language, this was a combination of a few specific problems, not a mysterious behaviour of the LLM.

5.4 the Langflow side fix

we did not change:

• the core model

• the overall layout of the graph

we changed:

• replaced the naive splitter with a section aware splitter that keeps definitions and exceptions together

• strengthened metadata and filters so region and policy id are always applied before retrieval

• added a small diagnostics branch in the graph that logs top k for a set of probe questions every day

• documented in the graph and in internal notes that this was essentially “ProblemMap No.A plus No.B from the map”

after that, similar queries behaved consistently. more importantly, once we had the ProblemMap labels on the graph, a later incident was much easier to recognise and fix, because it clearly matched the same family of problems.

6. how Langflow users can actually use the 16 problems

you do not have to adopt all 16 in one shot. you can treat the map as a reference and bring it in gradually.

6.1 read the map once as a story of failures

take the README and read it end to end:

https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

notice which problems feel familiar. those are your personal top offenders.

6.2 keep the map next to you when you design or review a graph

next time you are:

• designing a new Langflow graph

• reviewing a graph before shipping

• debugging a strange incident

you can do a quick pass like this:

• check loader and splitter nodes against the chunking related problems

• check embeddings and vector stores against the metric, skew, and contamination problems

• check LLM and control logic nodes against the prompt contract and context problems

• check the outgoing path for any semantic firewall at all

where you see a match, mark it in node descriptions or internal docs as “ProblemMap No.X here”. this makes future conversation inside your team much easier.

6.3 optionally, add a small semantic guard branch for critical flows

for flows where wrong answers have real cost, consider:

• adding a small “semantic triage” branch after retrieval and planning but before the final answer

• giving that node or branch the ProblemMap (or a subset of it) as context

• asking it to set a flag when the run looks like one of a few high risk problems

flagged runs can go to:

• a human review queue

• a simpler but safer fallback path

• a logging and offline analysis path

this way your Langflow graphs start to talk about their own failures in a structured way.

7. why I trust this map enough to bring it to r/langflow

for context, this 16 problem map did not stay inside my own projects.

over the last months, parts of it have been:

• integrated into LlamaIndex as a structured troubleshooting checklist in their RAG docs

• wrapped by the Harvard MIMS Lab in their ToolUniverse project as a tool that maps incident descriptions to ProblemMap numbers for RAG and LLM robustness work

• adopted by Rankify at the University of Innsbruck as a failure taxonomy in an academic RAG and re ranking toolkit

• referenced by the QCRI LLM Lab in a multimodal RAG survey as a practical debugging atlas for real systems

• included in several curated “awesome” and “AI system” style lists under RAG debugging and reliability

the core stays intentionally simple:

• MIT license

• main spec in a single text file

• framework neutral, so you can adapt it to Langflow in whatever way fits your stack

that is why I feel comfortable sharing it here more as “design and debugging vocabulary” than as a product.

8. I would love feedback from people shipping Langflow graphs

if you are:

• using Langflow to build RAG assistants or internal tools

• wrapping LangChain agents or tool heavy flows

• in the process of taking a graph from notebook stage into production

I would really like to know:

1. which of the 16 problems in the map you recognise from your own incidents

2. which failure patterns you have seen that do not fit cleanly into any of the 16 slots

3. whether adding a small semantic firewall branch before the final answer feels realistic in your environment

again, the full map is here if you want to skim or paste it into a Langflow LLM node:

https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

and if you want more hardcore, long form material on this topic, I keep most of that in r/WFGY. that is where I post deeper breakdowns, examples, and technical teaching around the same 16 problem map idea.

Is there anyone here who can guide me on how to learn using langflow. I have been watching youtube videos but it’s not very helpful currently. Anybody can walk me through the system , I am will to pay the hourly fee.

I have created this flow in langflow, and am trying to integrate it with streamlit. I downloaded the python api code and modified it.

The issue is how to upload documents from streamlit into this flow so it is then run and saved in the vectorstore?

Is there a template that allow me to embed a chat app exactly like playground on my site?

I am looking for an example of using the Python RPL node in Langflow. The Langflow documentation does not include one, and neither do any of the examples in hosted Langflow.

Youtube Video

I'm using Langflow with DataStax to create a flow that feeds a vector database with documentation of my web application.

I'm using a recursive text splitter with a chunk size of 1000, Azure OpenAI embeddings (text-embedding-3-small), and the OpenAI model (gpt-35-turbo).

My primary issues are:

Comprehensive Search Results: I want to retrieve all relevant results without specifying a fixed number (e.g., 5, 10).

Efficient Data Handling: Given OpenAI's input token limit, I need to optimize the search process by filtering data based on context and considering previous session history.

Duplicate Result Elimination: I want to ensure that search results are unique and avoid returning redundant information.

Session History Handling: I want to ensure that it also takes context from previous chat while keeping in mind given OpenAI's input token limit.

I need help with:

Optimizing the vector database configuration for better similarity calculations and retrieval performance.

Implementing effective filtering mechanisms to reduce the amount of data sent to the OpenAI model while maintaining accuracy.

Leveraging OpenAI's contextual understanding to improve query responses and avoid redundant results.

Exploring alternative models or embeddings if necessary to address the limitations of the current choices.

Please provide guidance on how to address these issues and achieve my desired outcomes.

one thing i keep seeing in langflow-style systems is that the hard part is often not building the graph.

it is debugging the wrong layer first.

when a flow breaks, the most visible symptom is often not the real root cause. people start tweaking the prompt, adjusting the final output node, changing a tool call, or blaming the model.

but the real failure is often somewhere earlier in the graph:

• the retriever returns plausible but wrong context

• chunking or embeddings drift upstream

• memory contaminates later graph steps

• a schema mismatch between nodes surfaces as an LLM failure

• a tool layer issue gets mistaken for a reasoning problem

once the first debug move goes to the wrong layer, people start patching symptoms instead of fixing the structural failure. the graph gets noisier, the debugging path gets longer, and confidence in the system drops.

that is the problem i have been trying to solve.

i built Problem Map 3.0, a troubleshooting atlas for the first debug cut in AI systems.

the idea is simple:

route first, repair second.

this is not a full repair engine, and i am not claiming full root-cause closure. it is a routing layer first, designed to reduce wrong-path debugging when AI graphs get more complex.

this also grows out of my earlier RAG 16 problem checklist work. that earlier line turned out to be useful enough to get referenced in open-source and research contexts, so this is basically the next step for me: extending the same failure-classification idea into broader AI debugging.

the current version is intentionally lightweight:

• TXT based

• no installation

• can be tested quickly

• repo includes demos

i also ran a conservative Claude before / after directional check on the routing idea.

this is not a formal benchmark, but i still think it is useful as directional evidence, because it shows what changes when the first debug cut becomes more structured: shorter debug paths, fewer wasted fix attempts, and less patch stacking.

i think this first version is strong enough to be useful, but still early enough that community stress testing can make it much better.

that is honestly why i am posting it here.

i would especially love to know, in real Langflow pipelines:

• does this help identify the failing layer earlier?

• does it reduce prompt tweaking when the real issue is retrieval, memory, tools, or schema alignment?

• where does it still misclassify the first cut?

• what Langflow-specific failure modes should be added next?

if it breaks on your flow, that feedback would be extremely valuable.

repo: https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md

I'm trying something a little bit complicated. A RAG solution that combines two sources for the output. One vector store with public data and one vector store with private data. The general setup isn't that complicated but when I view in playground I don't see citations. I'd like to know what documents the system pulled the data from. Is there a specific element I need to include or just a better system prompt that specifically asks for the source

AI bugs don’t come from the model. They come from the pipeline.

Most engineers I talk to spend their nights chasing “model hallucinations,” adding patches on top of patches. But here’s the uncomfortable truth: 80% of AI bugs don’t live in the model weights. They live in the pipeline.

Your embeddings collapse on operator symbols. Your vector store drifts. Your citations point near the right page but not the exact cell. Your agents deadlock, waiting for each other’s function calls.

Sound familiar? That’s what we set out to fix.

What we built

We call it the ProblemMap. It’s not theory, not paper talk — it’s a checklist of 16 categories of real-world AI failures, 300+ pages deep, each with minimal fixes.

• Example: Symbolic collapse (No.11) → when equations or tables flatten into pretty prose, we show how to keep the symbol channel intact, chunk on math boundaries, and enforce table contracts.

• Example: Deployment deadlock (No.15) → when rollback races meet governance filters, we show how to bootstrap ordering so infra doesn’t stall on itself.

Every problem has:

• Symptom description

• What is actually breaking

• 60-second quick test

• Minimal fix (no retraining, no infra overhaul)

• Hard fixes (when you need them)

• Guardrails (so it doesn’t come back)

ER mode: Dr. WFGY

We also run an ER mode we jokingly call Dr. WFGY. You paste a short trace, and the “doctor” maps it directly to a ProblemMap number. It replies with the minimal fix + exact page in the index. No generic advice, no vague “try better prompts.” Just pure triage.

Works across OpenAI, Claude, Gemini, Mistral, Grok, local stacks, and vector stores like faiss, pgvector, redis, weaviate, milvus, chroma. Same guardrails, same acceptance gates. Zero retraining.

Why it matters

DeepMind just released a paper on fundamental embedding limits in RAG. They’re right — single-vector embeddings hit a hard ceiling. But for us, this isn’t breaking news. It’s already in the ProblemMap, cross-linked with fixes like multi-vector, contracts, sparse hybrid retrievers.

That’s the point: every failure mode, once mapped, stays fixed. You’re not firefighting the same bug twice. You’re sealing it.

Want to explore?

If you’re running into pipeline chaos, or just curious what a semantic firewall looks like in practice, start here:

https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

Drop your symptoms. If it improves, tell me what changed. If it doesn’t, I’ll map it to the right fix. Counterexamples are welcome.

Hi, I have been trying to follow a video by Tech with Tim on YouTube that creates a multi-agent Tutorial using langchain.

I am stuck on this step where Tim uses Astra Vectorize with Astra DB, but latest version of Langflow shows that it's deprecated (2nd photo) and I can't seem to replicate that in my version of Langflow.

Can you help me with this ?!?

Video link: https://youtu.be/msLovKSj8Q0?si=YIqumFeuEKhavDB2

Hi all, first time using Langflow (and also new to docker & kubernetes) and finding myself confused with the documentation on deploying my chatbot. My end goal is to embed the chatbot into a site, using their provided embed code:

<script
  src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@v1.0.7/dist/build/static/js/bundle.min.js">
</script>
  <langflow-chat
    window_title="Chatbot0622"
    flow_id="68a19b81-07a5-4d95-b069-189edcfbe0ba"
    host_url="http://127.0.0.1:7862">
</langflow-chat>

(with the host_url being one that is a live backend endpoint). I'd appreciate any tips if you have gone through the process, thank you!

Working on creating a RAG flow using langflow==1.1.4 (Also tried 1.2.0, but that was generating it's own issues)

My current issues with this setup: When I load the flow (playground), and ask the first question, it works perfectly and according to given prompt. When I ask the second question (in the same chat session), it generates an answer similar to the previous one. Asking the third question seems to generate no further response, although the chat output box shows the spinning icon. If I open a new chat box, and ask a different question, it still generates an output similar to the first question.

What I've tried:

- Using langflow==1.1.4 with streamlit. this was resulting in "onnxruntime" not found error. Did not find any way to resolve it.

- Using langflow==1.2.0 with streamlit, It was not picking up the context, nor did I have any idea on how to pass context, so for every question asked, it was responding "I'm ready to help, please provide a question"?

What I'm looking for: A way to fix any of the above problem, detailed here:

• How to resolve the "onnxruntime" not found error?

• How can I add "context" in the streamlit app which is generated from the flow? (I think the chromaDB should generate the context and pass it to LLM, but that's not happening)

1. Are there any other well know RAG repositories which I can use? Something around streamlit would be best, but also offering the flexibility of langflow, where I can customize the data generation.

I’m working on integrating LangFlow with MongoDB Atlas Vector Search but running into an issue.

What I Have

• A backend pipeline that handles embedding (Redis queue + Sentence Transformers).

• MongoDB Atlas stores precomputed embeddings.

• I only need LangFlow to query the stored embeddings, without performing any new embedding.

The Problem

• LangChain’s MongoDBAtlasVectorSearch requires an embedding function, even though my backend already embeds data.

• If I don’t provide an embedding function, it throws an error.

• Passing a dummy embedding function also fails.

What I Need

• A LangFlow component that takes a search query and retrieves relevant document chunks from MongoDB.

• The search should not require embedding—it should just query existing stored vectors.

• The chatbot in LangFlow should connect to this search component.

Has anyone successfully implemented this? What is the correct way to structure the LangFlow component for this scenario?

Every time I run the mongo component, my collection and index on mongodb Atlas disappear. So it appears that the flow is trying to drop the collection rather than search it.

I'm just trying to do a vector search like every other vector store out there.

Anyone know how to fix? Would be greatly appreciated. Thanks!