How Flowise Works: Architecture and Data Flow Explained

I spent three weeks building RAG pipelines the hard way—writing prompt chains by hand, managing vector store connections through Python scripts, debugging state between API calls. Then I realized I was solving the same routing problem over and over. That’s when I looked at Flowise, and what surprised me wasn’t the drag-and-drop interface. It was what had to happen underneath to make that interface not lie to you.

The Problem: Complexity Hidden Behind Visual Simplicity

Building LLM applications involves wiring together discrete, stateful steps. You need to talk to a language model, yes, but before that you’re chunking documents, embedding them, storing them somewhere searchable, retrieving the right context, formatting it into a prompt, handling errors when the API times out, and—if you’re doing anything real—managing memory across multiple turns of conversation.

Most people try this with a Python script or a Jupyter notebook. It works until it doesn’t. You end up with utility functions scattered across three files, error handling that catches the wrong exceptions, and state that lives in global variables that you’re afraid to refactor.

The promise of Flowise is that you can build this entire pipeline visually without writing code. But that only works if the tool actually understands the shape of your problem. Let’s look at what has to happen for that to be true.

Core Architecture: Nodes, Edges, and Execution Order

At the center of Flowise is a directed acyclic graph (DAG). Every component you drag onto the canvas—an LLM, a retriever, a prompt template, a text splitter—is a node. Every connection between them is an edge. The visual editor lets you build this graph by dragging.

Behind the canvas, Flowise serializes this graph into JSON. Here’s roughly what that looks like:

{
  "nodes": [
    {
      "id": "chatOpenAI_0",
      "label": "ChatOpenAI",
      "name": "chatOpenAI",
      "type": "language",
      "baseClasses": ["BaseLLM"],
      "data": {
        "temperature": 0.7,
        "modelName": "gpt-4"
      }
    },
    {
      "id": "promptTemplate_0",
      "label": "PromptTemplate",
      "name": "promptTemplate",
      "baseClasses": ["BasePromptTemplate"],
      "data": {
        "template": "Answer this question: {question}"
      }
    }
  ],
  "edges": [
    {
      "source": "promptTemplate_0",
      "target": "chatOpenAI_0",
      "sourceHandle": "output",
      "targetHandle": "input"
    }
  ]
}

When you hit “Run” on a chatflow, Flowise traverses this graph from inputs to outputs. It doesn’t execute nodes in parallel; it respects the dependency order. The prompt template runs first, produces output, which feeds into the LLM node. That’s straightforward for simple chains.

But here’s where it gets interesting: Flowise needs to know which nodes are starting points (where does user input enter?) and which are sinks (where does the final answer come out?). It infers this from your graph topology, but you can also be explicit. In practice, if your graph doesn’t have exactly one clear path from input to output, the execution can become ambiguous, and you’ll see errors about “Circular dependencies detected” or “Multiple execution paths found.” This bit still feels fragile to me, even after watching it work correctly a hundred times.

Data Flow: From Canvas to LLM Call

Let me trace a real execution path. You build a simple chatflow: user message → retrieval-augmented generation (RAG) pipeline → LLM → response.

You connect a ChatMessage input node to a Retriever node (which points to a Pinecone vector database). The Retriever is connected to a Prompt Template that includes the retrieved context. That template feeds into an LLM node. The LLM output goes to a Chat History node that stores the exchange.

When a user sends a message through the API or the web interface, Flowise receives it, looks up the stored chatflow definition (JSON), and begins execution. Here’s what happens:

1. Input marshaling. The user message arrives as a string. Flowise wraps it in a message object with metadata (sender, timestamp, session ID). This becomes the payload flowing through the graph.

2. Retriever step. The Retriever node receives the message, extracts text from it, and constructs a query for Pinecone. It sends this query, gets back a list of document chunks scored by similarity, and formats them as retrieved context. This is where latency usually hurts—a vector database round trip adds 200-500ms to your execution.

3. Prompt assembly. The Prompt Template node receives both the original message and the retrieved chunks. It interpolates them into your template string. If your template says Context: {context}nQuestion: {input}, Flowise substitutes the actual values here.

4. LLM call. The Chat node (or the underlying LLM it wraps) receives the assembled prompt and calls the model. This is where the real latency lives—anywhere from 500ms to 30 seconds depending on the model and response length.

5. Post-processing and storage. The LLM output comes back. If you’ve connected a Chat History node, Flowise stores the input-output pair in memory (or in a connected database like PostgreSQL). The response is returned to the caller.

All of this is asynchronous under the hood. Flowise uses Node.js and Express, so I/O blocking isn’t a problem. But the DAG execution itself is sequential—each step waits for the previous one to complete. If you need parallel branches, you have to build that into your node logic.

Vector Databases and Embeddings: Where Complexity Lives

The part of Flowise that actually made me sit up was the embedding pipeline. When you drag a Document Loader and a Text Splitter and a vector store onto the canvas, you’re not just moving data around. You’re saying: “Take these documents, break them into chunks, convert chunks to vectors, and store them somewhere retrievable.”

Flowise handles this in two phases: ingest and query.

During ingest (which you typically do once, or when documents change), Flowise:

1. Loads documents from a source (PDF, text file, URL, etc.)
2. Splits them into chunks using a strategy you specify (character count, sentence, recursive, etc.)
3. Converts each chunk to a vector using an embedding model (OpenAI’s text-embedding-3-small, local models via Ollama, etc.)
4. Stores these vectors and their metadata in a vector database (Pinecone, Weaviate, Supabase, Chroma, etc.)

During query, when a user asks a question, the Retriever node:

1. Embeds the user’s question using the same embedding model
2. Queries the vector database for the K nearest neighbors (usually K=4 or K=8)
3. Returns the original document chunks (not just vectors) as context

The critical constraint: the embedding model used for ingest must be the same as the one used at query time. If you ingest documents with OpenAI’s embeddings but query with local embeddings, you’ll get garbage results—vectors from different models don’t live in the same semantic space. Flowise doesn’t prevent you from making this mistake, which is annoying. I’ve seen it happen. The queries run successfully but return irrelevant chunks because the vector spaces don’t align.

If you’re self-hosting with local models via Ollama, this is usually fine—you pick one embedding model and stick with it. But if you’re mixing cloud APIs and local models, you need to be careful.

Node Types and How They Compose

Flowise ships with a library of pre-built nodes. Each node is a wrapper around some underlying library (usually LangChain components, but increasingly native implementations). When you add a node, you’re really instantiating a class that knows how to:

• Accept inputs from upstream nodes
• Run some logic or call some service
• Produce outputs for downstream nodes
• Handle errors and timeouts
• Serialize its configuration to JSON

Common node categories:

• LLMs: Chat models (ChatGPT, Claude, Llama via Ollama), embeddings models, older completion APIs.
• Memory: Buffer Window Memory, Summary Memory, vector store-based memory for long conversations.
• Retrievers: Vector store retrievers, traditional search (BM25), SQL retrievers.
• Tools: Web search, calculator, custom API calls. These let your LLM interact with the outside world.
• Agents: Meta-nodes that wrap an LLM and a set of tools, letting the LLM decide when to use which tool. This is where it gets agentic.
• Document loaders: PDFs, Notion, GitHub, URLs, YouTube transcripts.
• Text splitters: Chunk documents by character, token, or semantic boundary.

The constraint is that nodes can only be connected if their types match. An LLM node expects a BasePromptTemplate input, not a raw string. The canvas editor enforces this, but under the hood it’s just type-checking on the base classes. This prevents you from building nonsensical graphs, which is useful for a visual tool.

State and Persistence: Where Things Get Sticky

Flowise stores two kinds of state: chatflow definitions and conversation history.

Chatflow definitions (the JSON DAG) are stored in SQLite by default, but you can point it to PostgreSQL. When you save a chatflow in the UI, Flowise writes it to the database. When you run a flow, it reads the definition back, deserializes it, and builds the in-memory graph.

Conversation history is trickier. If you have a Chat History node in your flow, Flowise needs to store and retrieve past messages. By default, it uses in-memory storage, which means it’s lost when the container restarts. For anything production-like, you need to connect a database. Flowise supports MySQL, PostgreSQL, and vector stores for storing conversation memory.

This is where I hit my first real friction. I deployed Flowise in Docker without configuring a persistent database, ran some RAG queries, then restarted the container for an update. The conversation history vanished. The chatflow definition was still there, but the context was gone. It should have been obvious, but I assumed Flowise would default to something persistent. It didn’t. Now I always docker-compose it with PostgreSQL and set the DATABASE_URL environment variable.

version: '3.8'
services:
  flowise:
    image: flowiseai/flowise:latest
    environment:
      - PORT=3000
      - DATABASE_URL=postgresql://user:password@postgres:5432/flowise
      - APIKEY_ENCRYPTION_KEY=your-secret-key
    ports:
      - "3000:3000"
    depends_on:
      - postgres
  
  postgres:
    image: postgres:15
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=flowise
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

With this setup, both the chatflow definitions and conversation history survive restarts. The tradeoff is that you’re now managing another service, and database queries add a few milliseconds to each execution.

Integration Points and Extensibility

Flowise talks to external services at multiple points. The most obvious are LLM APIs (OpenAI, Anthropic, etc.), but there are also vector databases, document storage (S3, local filesystem), and custom tools.

For custom logic that isn’t covered by built-in nodes, Flowise lets you add a Custom Tool or Custom Node. A custom tool is JavaScript code that gets executed in a sandboxed context. You can write a function that calls your own API, does some calculation, and returns a result. This is useful but limited—you’re constrained to what you can do in JavaScript, and there’s no great error handling if your code crashes.

For deeper customization, you can fork Flowise and add custom nodes at the codebase level. The project is TypeScript and fairly modular. I haven’t done this myself, but I’ve read the source enough to know it’s possible. Most people don’t need to, though. The built-in nodes cover the common cases.

Reliability and Failure Modes

Flowise is stable for chatbots and RAG pipelines, but it has limits. When things fail, they usually fail in one of these ways:

Timeout cascades. If the LLM API is slow, your chat request hangs. Flowise doesn’t have a default timeout for individual nodes, so a slow API can stall your entire flow. You have to set timeouts manually per node.

Vector database inconsistency. If you update documents while queries are in flight, you can get stale results. Flowise doesn’t handle this—it’s a problem at the application level.

Memory leaks in long-running agents. If you build an agentic flow that loops (the agent decides to call a tool, processes the result, decides to call another tool), it can get stuck in an infinite loop if the agent doesn’t learn to stop. This isn’t Flowise’s fault—it’s a limitation of agents in general—but it’s worth knowing about.

For a homelab or small-scale deployment, these aren’t catastrophic. For a production system handling real traffic, you’d want monitoring, circuit breakers, and retry logic, most of which you’d have to add yourself.

The honest take is that Flowise is great for rapid prototyping and for building custom AI tools that would take weeks to code by hand. It’s not a framework that disappears—you’re always aware you’re building in a visual system. But that trade-off, being able to see your entire pipeline at once and adjust it without touching code, is worth it for most people.

FAQ

How much RAM does Flowise need to run?

Flowise itself runs on about 512MB, but your total memory use depends heavily on what models you’re using. If you’re running a local embedding model via Ollama alongside Flowise, add another 4-8GB. For vector database operations, another 2GB. On a homelab, 16GB is comfortable; 8GB is tight but workable for small deployments.

Can Flowise run on a Raspberry Pi?

Technically yes for the UI and simple flows, but practically no for anything involving local LLMs. A Pi 4 with 8GB can barely run Ollama and Flowise simultaneously. You’ll get timeouts and out-of-memory errors. Desktop hardware or a cheap cloud VM is a better fit.

Do I have to use OpenAI or can I use local models?

You can use local models via Ollama without spending anything on API calls. Connect Ollama as an LLM node, same as you would OpenAI. Embedding models can also be local (using ollama or sentence-transformers). The main cost becomes your own hardware.

What happens to my chatflows if Flowise crashes?

Chatflow definitions are persisted to the database, so they survive restarts. Conversation history only survives if you’ve configured a persistent database (PostgreSQL or MySQL). If you’re using the default SQLite with in-memory history, conversations are lost.

Can Flowise connect to my existing vector database?

Yes. Flowise supports Pinecone, Weaviate, Supabase, Chroma, Milvus, and others. Point the Vector Store Retriever node to your database, set the API key or connection details, and it works. Just make sure you’ve already ingested documents into that database before querying.

Explore Flowise in our AI Homelab Toolkit.