Langflow

A visual workflow builder for creating AI-powered agents and applications.

Overview

Langflow provides a drag-and-drop interface for building AI workflows without writing code. Connect components visually, test in real-time, and deploy as APIs or integrations.

Key Features

• Visual builder: Drag-and-drop interface for creating workflows
• Component library: Pre-built blocks for LLMs, prompts, tools, and more
• Live playground: Test and iterate on flows instantly
• Python access: Customize any component with code when needed
• API deployment: Turn flows into REST APIs automatically
• Multi-agent: Orchestrate multiple AI agents in a single workflow

Getting Started

Opening Langflow

1. Launch Langflow from the Hub
2. The visual editor opens with a blank canvas
3. Start building by dragging components from the sidebar

Your First Flow

Create a simple chatbot:

1. Add an Input: Drag a “Chat Input” component to the canvas
2. Add an LLM: Drag an “OpenAI” or other LLM component
3. Add an Output: Drag a “Chat Output” component
4. Connect them: Draw lines between the components
5. Test it: Click “Run” and chat with your flow

Core Concepts

Components

Building blocks for your workflows:

Connections

Connect components by dragging from output ports to input ports. The editor validates connections to ensure compatibility.

Variables

Use variables to make flows dynamic:

• {input} - User input
• {context} - Retrieved context
• Custom variables defined in your flow

Building Workflows

Chatbot with Memory

Create a chatbot that remembers conversation history:

1. Chat Input → Memory → LLM → Chat Output
2. Configure memory to store recent exchanges
3. The LLM receives conversation context automatically

RAG Pipeline

Build a document Q&A system:

1. File Input → Receives documents
2. Text Splitter → Chunks documents
3. Embeddings → Converts to vectors
4. Vector Store → Stores for retrieval
5. Retriever → Finds relevant chunks
6. LLM → Generates answers
7. Chat Output → Returns response

Multi-Agent Workflow

Orchestrate multiple agents:

1. Router Agent → Decides which specialist to use
2. Research Agent → Gathers information
3. Writer Agent → Creates content
4. Review Agent → Checks quality
5. Output → Returns final result

Testing & Debugging

Playground

The built-in playground lets you:

• Run flows step-by-step
• Inspect component inputs/outputs
• View intermediate results
• Debug connection issues

Logs

View detailed logs for each run:

• Token usage
• Response times
• Error messages
• Full request/response data

Exporting & Sharing

Export as JSON

Download your flow as a JSON file to:

• Version control in Git
• Share with teammates
• Import into other Langflow instances

Deploy as API

Turn any flow into a REST endpoint:

1. Click “API” in the flow editor
2. Copy the generated endpoint
3. Send requests to your flow programmatically

curl -X POST "your-flow-endpoint" \
  -H "Content-Type: application/json" \
  -d '{"input": "Hello!"}'

LLM Configuration

Supported Providers

Configure your preferred AI providers:

• OpenAI: GPT-4, GPT-3.5
• Anthropic: Claude models
• Azure OpenAI: Enterprise Azure models
• Ollama: Self-hosted open models
• AWS Bedrock: AWS-hosted models
• Google: Gemini models

API Keys

Set API keys in Langflow’s settings or use environment variables configured in your workspace.

Best Practices

1. Start simple: Build basic flows before adding complexity
2. Test often: Use the playground to verify each step
3. Modular design: Create reusable sub-flows
4. Version your flows: Export and commit JSON files to Git
5. Monitor costs: Watch token usage for LLM-heavy flows

Use Cases

• Customer support bots with knowledge base integration
• Content generation pipelines for blogs and documentation
• Data extraction workflows from documents and websites
• Research assistants that search and summarize
• Code review agents for development workflows

Documentation