Langflow: Visual AI Workflow Builder

Drag, Drop, Deploy

AI workflows can get complicated. You need to chain prompts, add retrieval from documents, include memory, call external APIs, and handle branching logic. Writing all of that in code is doable but time-consuming.

Langflow gives you a visual canvas where each step is a block. Connect blocks with lines. Configure settings by clicking. Test the whole flow in real time. Deploy when ready.

Building Blocks

LLM nodes. Connect to OpenAI, Anthropic, Google, Ollama, or any compatible model. Configure temperature, system prompts, and output parsing.

Document loaders. Pull content from PDFs, websites, databases, APIs, or file systems. Feed that content into your AI pipeline.

Vector stores. Store and retrieve document embeddings for RAG applications. Support for Pinecone, Chroma, Weaviate, and more.

Memory nodes. Give your AI conversations context and history. Short-term buffer memory or long-term persistent storage.

Tool nodes. Web search, code execution, API calls, database queries. Give your AI the ability to take actions.

Logic nodes. Conditional branching, loops, routers, and filters. Build sophisticated decision trees visually.

What People Build

Research assistants that search multiple sources, synthesize findings, and produce structured reports. Each step is visible and tweakable.

Customer support flows that classify incoming tickets, retrieve relevant documentation, generate responses, and escalate when confidence is low.

Data processing pipelines that extract information from documents, validate it against business rules, and load it into databases.

Content workflows that generate drafts, run quality checks, adapt for different platforms, and prepare final versions for review.

Why Visual Matters

When your AI pipeline is code, debugging means reading logs and adding print statements. When it is visual, you can see exactly where data flows, where it transforms, and where things break.

Click any connection between blocks and see the actual data passing through. This makes debugging intuitive and building iterative. Non-technical team members can understand what the pipeline does just by looking at it.

Getting Started

Langflow runs locally or in the cloud. The fastest start:

pip install langflow
langflow run

Open the web interface and start dragging blocks. The template gallery has pre-built flows for common use cases. Modify a template rather than starting from scratch — it is faster and teaches you the patterns.

Langflow is open source and backed by DataStax. The community is active and the documentation is thorough.

Discover workflow and automation tools on a-gnt