Langflow Visual AI Builder

Deploy Langflow on Clore.ai — build and run visual AI pipelines, RAG systems, and multi-agent workflows on affordable GPU cloud infrastructure with a drag-and-drop no-code interface.

Overview

Langflow is an open-source, low-code platform for building AI applications using a visual drag-and-drop interface. With 55K+ GitHub stars and a rapidly growing community, it has become one of the go-to tools for prototyping and deploying LLM-powered workflows without writing complex boilerplate code.

Langflow is built on top of LangChain and provides a graphical editor where you can connect components — LLMs, vector stores, document loaders, retrievers, agents, tools — by drawing lines between nodes. The resulting pipeline can be exported as an API endpoint, shared as a template, or embedded into your application.

Key capabilities:

• Visual RAG builder — Connect document loaders → embeddings → vector stores → retrievers in minutes

• Multi-agent workflows — Chain multiple AI agents with memory, tools, and decision logic

• LangChain integration — Access the full LangChain ecosystem via UI nodes

• Component marketplace — Community-contributed components for dozens of services

• API-first — Every flow auto-generates a REST API endpoint

• Memory and state — Built-in conversation memory, session management

Why Clore.ai for Langflow?

Langflow's compute requirements are minimal — it's a Python web server handling workflow orchestration. However, Clore.ai opens up powerful use cases:

• Self-hosted embeddings — Run local embedding models (nomic-embed, BGE) on GPU for fast, free vector generation

• Local LLM backends — Connect Langflow to Ollama or vLLM running on the same server

• Private data pipelines — Process sensitive documents without sending data to external APIs

• Cost optimization — Replace expensive OpenAI embedding calls with free local inference

• Persistent workflows — Long-running flows on dedicated servers (vs. ephemeral cloud functions)

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Requirements

Langflow itself is lightweight and CPU-based. GPU is optional but enables free local LLM/embedding inference.

Configuration	GPU	VRAM	RAM	Storage	Est. Price
Langflow only (API backends)	None needed	—	4 GB	10 GB	~$0.03–0.08/hr
+ Local embeddings (nomic-embed)	RTX 3090	24 GB	8 GB	20 GB	~$0.20/hr
+ Ollama (Llama 3.1 8B)	RTX 3090	24 GB	16 GB	40 GB	~$0.20/hr
+ Ollama (Qwen2.5 32B)	RTX 4090	24 GB	32 GB	60 GB	~$0.35/hr
+ vLLM (production RAG)	A100 80GB	80 GB	64 GB	100 GB	~$1.10/hr

For a comparison of GPU options on Clore.ai, see the GPU Comparison Guide.

Software requirements on the Clore.ai server:

• Docker Engine (pre-installed on all Clore.ai images)

• NVIDIA Container Toolkit (pre-installed on GPU images, required only for local LLM)

• 10+ GB free disk space for the Langflow image and flow data

• Outbound internet access (for pulling Docker images and reaching external APIs)

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Quick Start

Step 1: Connect to Your Clore.ai Server

Book a server on Clore.ai marketplace. For Langflow-only usage, any server with ≥4 GB RAM works. Connect via SSH:

ssh root@<server-ip> -p <ssh-port>

Step 2: Run Langflow with Docker

The simplest deployment — single command:

docker run -d \
  --name langflow \
  --restart unless-stopped \
  -p 7860:7860 \
  langflowai/langflow:latest

Wait ~30–60 seconds for startup, then access at http://<server-ip>:7860

Step 3: Expose Port 7860 on Clore.ai

In the Clore.ai dashboard, navigate to your server → Ports section → ensure 7860 is mapped. If using a custom template, add 7860:7860 to your port configuration before starting the server.

Step 4: First Launch

On first visit, Langflow will:

1. Show a welcome screen and ask to create an account (or skip)

2. Offer a set of starter templates (RAG, chatbot, agent)

3. Open the visual canvas editor

You're ready to build your first flow!

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Configuration

Persistent Data Storage

By default, Langflow stores flows and data inside the container. Mount a volume to persist across restarts:

mkdir -p /opt/langflow/data

docker run -d \
  --name langflow \
  --restart unless-stopped \
  -p 7860:7860 \
  -v /opt/langflow/data:/app/langflow \
  -e LANGFLOW_DATABASE_URL=sqlite:////app/langflow/langflow.db \
  langflowai/langflow:latest

Environment Variables Reference

docker run -d \
  --name langflow \
  --restart unless-stopped \
  -p 7860:7860 \
  -v /opt/langflow/data:/app/langflow \
  -e LANGFLOW_HOST=0.0.0.0 \
  -e LANGFLOW_PORT=7860 \
  -e LANGFLOW_DATABASE_URL=sqlite:////app/langflow/langflow.db \
  -e LANGFLOW_SECRET_KEY=your-secret-key-here \
  -e LANGFLOW_AUTO_LOGIN=false \
  -e LANGFLOW_SUPERUSER=admin \
  -e LANGFLOW_SUPERUSER_PASSWORD=your-password \
  -e LANGFLOW_WORKERS=2 \
  -e LANGFLOW_LOG_LEVEL=info \
  langflowai/langflow:latest

Variable	Description	Default
LANGFLOW_HOST	Bind address	0.0.0.0
LANGFLOW_PORT	Web server port	7860
LANGFLOW_DATABASE_URL	Database connection string	SQLite in memory
LANGFLOW_SECRET_KEY	Session secret (set for production)	Random
LANGFLOW_AUTO_LOGIN	Skip login screen	true
LANGFLOW_SUPERUSER	Admin username	admin
LANGFLOW_SUPERUSER_PASSWORD	Admin password	—
LANGFLOW_WORKERS	Number of API workers	1
LANGFLOW_LOG_LEVEL	Logging verbosity	critical
OPENAI_API_KEY	Pre-load OpenAI key	—

Using PostgreSQL (Production)

For multi-user or production deployments, use PostgreSQL instead of SQLite:

# Start PostgreSQL
docker run -d \
  --name langflow-db \
  --restart unless-stopped \
  -e POSTGRES_USER=langflow \
  -e POSTGRES_PASSWORD=langflow-secret \
  -e POSTGRES_DB=langflow \
  -v langflow-pgdata:/var/lib/postgresql/data \
  postgres:16-alpine

# Start Langflow with PostgreSQL backend
docker run -d \
  --name langflow \
  --restart unless-stopped \
  --link langflow-db:db \
  -p 7860:7860 \
  -v /opt/langflow/data:/app/langflow \
  -e LANGFLOW_DATABASE_URL=postgresql://langflow:langflow-secret@db:5432/langflow \
  -e LANGFLOW_SECRET_KEY=$(openssl rand -hex 32) \
  -e LANGFLOW_AUTO_LOGIN=false \
  -e LANGFLOW_SUPERUSER=admin \
  -e LANGFLOW_SUPERUSER_PASSWORD=changeme \
  langflowai/langflow:latest

Docker Compose (Full Stack)

For a complete setup with PostgreSQL and Nginx reverse proxy:

# /opt/langflow/docker-compose.yml
version: "3.9"

services:
  langflow:
    image: langflowai/langflow:latest
    restart: unless-stopped
    ports:
      - "7860:7860"
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://langflow:secret@db:5432/langflow
      - LANGFLOW_SECRET_KEY=${SECRET_KEY:-changeme}
      - LANGFLOW_AUTO_LOGIN=false
      - LANGFLOW_SUPERUSER=admin
      - LANGFLOW_SUPERUSER_PASSWORD=${ADMIN_PASSWORD:-changeme}
      - LANGFLOW_WORKERS=2
    volumes:
      - langflow-data:/app/langflow
    depends_on:
      db:
        condition: service_healthy

  db:
    image: postgres:16-alpine
    restart: unless-stopped
    environment:
      - POSTGRES_USER=langflow
      - POSTGRES_PASSWORD=secret
      - POSTGRES_DB=langflow
    volumes:
      - pgdata:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U langflow"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  langflow-data:
  pgdata:

cd /opt/langflow
SECRET_KEY=$(openssl rand -hex 32) ADMIN_PASSWORD=yourpassword docker compose up -d

Specific Version Pinning

For reproducible deployments, pin a specific version:

# List available versions: https://hub.docker.com/r/langflowai/langflow/tags
docker run -d \
  --name langflow \
  -p 7860:7860 \
  langflowai/langflow:1.1.4

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GPU Acceleration (Local Model Integration)

Langflow itself runs on CPU, but connecting it to local GPU-powered services on the same Clore.ai server unlocks free, private inference.

Connect Langflow to Ollama

# Step 1: Start Ollama with GPU
docker run -d \
  --name ollama \
  --gpus all \
  --restart unless-stopped \
  -p 11434:11434 \
  -v ollama-models:/root/.ollama \
  ollama/ollama:latest

# Step 2: Pull models for different use cases
docker exec ollama ollama pull llama3.1:8b          # General chat
docker exec ollama ollama pull nomic-embed-text     # Embeddings for RAG
docker exec ollama ollama pull qwen2.5-coder:7b    # Code generation

# Step 3: Start Langflow with Ollama network access
docker run -d \
  --name langflow \
  --restart unless-stopped \
  -p 7860:7860 \
  -v /opt/langflow/data:/app/langflow \
  --add-host host.docker.internal:host-gateway \
  langflowai/langflow:latest

In Langflow UI, use the Ollama component with:

• Base URL: http://host.docker.internal:11434

• Model: llama3.1:8b

For embeddings, use the OllamaEmbeddings component with:

• Base URL: http://host.docker.internal:11434

• Model: nomic-embed-text

Full Ollama configuration: see the Ollama guide

Connect Langflow to vLLM (OpenAI-compatible)

# Start vLLM with OpenAI-compatible API
docker run -d \
  --name vllm \
  --gpus all \
  --restart unless-stopped \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model mistralai/Mistral-7B-Instruct-v0.3 \
  --gpu-memory-utilization 0.85

# In Langflow, use the OpenAI component with custom base URL:
# Base URL: http://host.docker.internal:8000/v1
# API Key: (any value, e.g. "none")
# Model: mistralai/Mistral-7B-Instruct-v0.3

Full vLLM configuration: see the vLLM guide

Building a Local RAG Pipeline

Example RAG flow using only local models (zero API cost):

1. File Loader node → Load PDF/text documents

2. Text Splitter node → Chunk documents (size: 512, overlap: 50)

3. OllamaEmbeddings node → Generate embeddings (model: nomic-embed-text)

4. Chroma or FAISS node → Store vectors locally

5. OllamaEmbeddings node → Embed the user's query

6. Retriever node → Find top-k similar chunks

7. Ollama node → Generate answer (model: llama3.1:8b)

8. Chat Output node → Return response

This entire pipeline runs on your Clore.ai server with zero external API calls.

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Tips & Best Practices

1. Export Flows as Backups

Before stopping your Clore.ai server, export your flows:

• In the UI: Flows → Select all → Export → Download JSON

• Or via API: curl http://localhost:7860/api/v1/flows/

Store them in a persistent volume or download to your local machine.

2. Use the API for Automation

Every Langflow flow generates an API endpoint. Trigger flows programmatically:

# Get your flow ID from the UI (shown in the URL)
FLOW_ID="your-flow-id-here"

curl -X POST \
  "http://<server-ip>:7860/api/v1/run/$FLOW_ID" \
  -H "Content-Type: application/json" \
  -d '{
    "input_value": "Summarize the latest AI research papers",
    "input_type": "chat",
    "output_type": "chat"
  }'

3. Secure Your Instance

For anything beyond local testing:

# Always set authentication
-e LANGFLOW_AUTO_LOGIN=false \
-e LANGFLOW_SUPERUSER=admin \
-e LANGFLOW_SUPERUSER_PASSWORD=$(openssl rand -base64 16)

# Use a strong secret key
-e LANGFLOW_SECRET_KEY=$(openssl rand -hex 32)

4. Monitor Memory Usage

Langflow can accumulate memory over time with many active flows:

docker stats langflow
# If memory grows unbounded, restart periodically:
docker restart langflow

5. Use Starter Templates

Langflow ships with production-ready templates:

• Basic RAG — Document Q&A with vector store

• Memory Chatbot — Conversational agent with history

• Research Assistant — Web search + LLM synthesis

• Access via: New Flow → Starter Projects

6. Component Caching

Enable caching to speed up repeated flow runs:

• In flow settings: Enable "Cache" on expensive nodes (embeddings, LLM calls)

• Particularly useful for RAG retrieval during development

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Troubleshooting

Container Fails to Start

# Check logs for errors
docker logs langflow --tail 50

# Common issue: port already in use
lsof -i :7860
# Kill the conflicting process or change the port:
docker run ... -p 7861:7860 ...

UI Loads but Flows Don't Run

# Check worker process status
docker exec langflow ps aux | grep langflow

# Check for Python package errors
docker logs langflow 2>&1 | grep -i error

# Restart the container
docker restart langflow

Can't Connect to Ollama

# Test connectivity from inside the Langflow container
docker exec langflow curl http://host.docker.internal:11434/api/tags

# If --add-host flag is missing, recreate the container with:
--add-host host.docker.internal:host-gateway

# Verify Ollama is running
docker ps | grep ollama
curl http://localhost:11434/api/tags

Database Errors on Restart

# If using SQLite and getting lock errors:
docker stop langflow
ls -la /opt/langflow/data/
# Check for stale .lock files
rm -f /opt/langflow/data/langflow.db-wal
rm -f /opt/langflow/data/langflow.db-shm
docker start langflow

Slow Flow Execution

# Increase workers for parallel processing
-e LANGFLOW_WORKERS=4

# For embedding-heavy workloads, ensure GPU is used by Ollama:
docker exec ollama nvidia-smi
# Should show GPU utilization when embedding

Reset Admin Password

docker exec -it langflow python -c "
from langflow.services.database.utils import initialize_db
from langflow.services.deps import get_settings_service
# Use Langflow CLI instead:
"
# Use the CLI method:
docker exec -it langflow langflow superuser --username admin --password newpassword

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Further Reading

• Langflow GitHub Repository — Source code, issues, changelog

• Langflow Documentation — Official docs, component reference, API docs

• Langflow Discord — Community support and flow sharing

• Ollama on Clore.ai — Set up local LLM backend for Langflow

• vLLM on Clore.ai — High-throughput LLM serving for production flows

• GPU Comparison Guide — Choose the right Clore.ai GPU for your workload

• LangChain Documentation — Underlying framework reference

• Docker Hub: langflowai/langflow — Available image tags and versions