What is the difference between LangFlow and Flowise?

Both are visual LLM workflow builders. LangFlow is Python-native and targets developers who want to write custom components or export clean Python code from their flows. Flowise is more no-code oriented, with a simpler canvas. The two share the LangChain lineage but have diverged in developer profile and ecosystem.

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Build, host and run AI solutions.

LangFlow

Visual LLM workflow builder — drag-and-drop AI agents and RAG pipelines, deploy as a REST API in one click.

2 GB minimum, 4 GB recommended RAM 2 vCPU minimum Available

LangFlow (153 000+ GitHub stars, MIT, v1.11.x) is the leading open-source visual editor for building LLM applications and AI agent workflows. You drag components onto a canvas — LLM providers (OpenAI, Anthropic, Ollama, Mistral, Groq), vector databases (Qdrant, Chroma), document loaders, text splitters, memory modules, custom Python code — and wire them together into a working pipeline without writing a framework from scratch.

Self-hosted on a ServOrbit VPS, LangFlow gives your team a private workspace for prototyping and deploying AI pipelines. Each flow is automatically exposed as a REST API endpoint, ready to call from any application. Authentication is built in: a generated admin password locks your instance from the first boot. Data persists in an SQLite database on a named Docker volume — no PostgreSQL required to get started.

LangFlow complements the ServOrbit AI catalogue: pair it with Ollama for a fully local LLM backend, use Qdrant as the vector store for your RAG pipelines, or feed outputs through Langfuse for observability. MIT licence — no seat limits, no cloud dependency, no telemetry.

Key features

Drag-and-drop canvas to wire LLMs, vector databases, document loaders and custom Python components into a working AI pipeline — no framework boilerplate.

100+ built-in components covering major LLM providers (OpenAI, Anthropic, Ollama, Mistral, Groq, Azure), vector databases (Qdrant, Chroma, Pinecone) and tool integrations.

One-click REST API — every flow is instantly exposed as a documented HTTP endpoint, callable from any language or application.

Python-native: write custom components in pure Python, import any PyPI package, and unit-test your logic independently of the canvas.

Built-in auth: admin account is created with a generated password on first boot — no anonymous access possible with LANGFLOW_AUTO_LOGIN=false.

MIT licence — no seat limits, no telemetry, no vendor lock-in. All flow data stays on your VPS.

When to use this solution?

Prototype AI pipelines before writing code

Wire an LLM provider, a document loader and a vector database together on the canvas in minutes, validate the output, then call the auto-generated API directly from your application.

Build RAG chatbots on private documents

Load internal PDFs or markdown files, split them into chunks, embed them in Qdrant, and chain a retrieval step with an Ollama or OpenAI call — all without writing a RAG framework.

Share and version team AI workflows

Export each flow as a JSON file, store it in Git, and import it on another LangFlow instance. Your pipelines are reproducible, reviewable and deployable anywhere.

Deploy LangFlow on your VPS

Guide optimized for ServOrbit Cloud VPS.

Open LangFlow

After installing from the Marketplace, open the URL in your ServOrbit client area. Without a domain, open an SSH tunnel — ssh -L 7860:127.0.0.1:7860 root@your-vps-ip — then browse to http://localhost:7860.

Log in for the first time

Enter admin as the username and the generated password shown under ADMIN_PASSWORD in your ServOrbit client area. This account has full access to all flows and settings.

Create your first flow

Click New Flow → Blank Flow. Drag a Chat Input component and an OpenAI (or Ollama) LLM component onto the canvas. Connect them and click Run to send a test message.

Call the flow as a REST API

Click the API button (top-right). LangFlow shows the flow's unique endpoint URL and a ready-to-copy curl command. Call it from Node.js, Python or any HTTP client — no extra setup.

Export and version your flows

Go to Settings → Export to download the flow as a JSON file. Commit it to your Git repository and restore it on any LangFlow instance by importing the JSON file.

Frequently asked questions

Enter admin as the username and the generated password (ADMIN_PASSWORD in your ServOrbit client area). Authentication is enforced from the first boot — the instance is never open without credentials.

No. Access it via an SSH tunnel (ssh -L 7860:127.0.0.1:7860 root@your-ip, then http://localhost:7860). Attaching a domain enables direct HTTPS access — ServOrbit configures nginx and TLS automatically.

Yes. Use the Ollama component on the canvas and set its base URL. If Ollama is installed on the same VPS via the Marketplace, reach it at http://host.docker.internal:11434 from inside the LangFlow container.

Export each flow individually from the canvas menu (JSON export). For a full backup, copy the SQLite database: docker cp langflow-langflow-1:/app/data/langflow.db ./langflow-backup.db. Restore by copying it back and restarting the container.

Embed the deploy button

Maintaining a project that uses LangFlow? This button lets your readers deploy it on a VPS in one click, without reading Docker documentation.

Markdown

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HTML

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The button points to a VPS order with the template preselected. The image is served from servorbit.com — nothing to host on your side.

Complementary solutions

Automation & Workflows

Flowise

Build LLM automation pipelines with drag-and-drop. Visual LangChain chains, connected to your existing tools.

Artificial Intelligence

Ollama

Run open-source LLMs locally via a dead-simple API. Pull Llama 3, Mistral, Qwen or DeepSeek in one command — OpenAI-compatible, zero per-token cost.

Artificial Intelligence

Qdrant

Self-hosted vector database for semantic search and RAG. Store, index and query high-dimensional embeddings with HNSW indexing — the missing infrastructure layer for your LLM stack.

Artificial Intelligence

Langfuse

Open-source LLM observability: trace every AI request, run evaluations, manage prompt versions and compare model costs — self-hosted, compatible with OpenAI, LiteLLM, LangChain and Ollama.

Build, host and run AI solutions.

Activate LangFlow on your infrastructure.

Dedicated Cloud VPS — IPv4 included, European datacenter, support included. Your data never leaves your server.

Recommended configuration: 2 GB minimum, 4 GB recommended RAM · 2 vCPU minimum

Need help?

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