Langflow
langflow-ai/langflow
Visual builder for LLM flows — drag nodes for prompts, tools, and RAG without starting from a blank repo. GitHub: langflow-ai/langflow (~153k stars).
GitHub repository
Overview
Langflow is the open-source project at langflow-ai/langflow on GitHub (https://github.com/langflow-ai/langflow). Visual builder for LLM flows — drag nodes for prompts, tools, and RAG without starting from a blank repo. Category on LimeDock: ai-powered. Approximate community size: ~153k GitHub stars. Repo (copy/paste): https://github.com/langflow-ai/langflow This LimeDock Directories page explains what it is and how teams use it in plain English — so searches for the GitHub project can land on a practical guide.
Work with LimeDock
Using this skill? LimeDock can wire it into a durable automation you own.
Agents are a starting point. LimeDock turns the workflow into production automation your SaaS team owns — not another prompt library.
We sell owned automations for SaaS teams — live workflows that plug into Slack, CRM, and your internal platform — not just a skill list.
Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/langflow-ai/langflow GitHub path: langflow-ai/langflow
How to open it: 1. Copy this URL: https://github.com/langflow-ai/langflow 2. Clone or follow the README install for your OS / stack. 3. Start with the smallest example in their docs before production use.
How to use it
**Simple example** Non-experts need to prototype an agent flow your engineers can later harden.
1. Run Langflow locally or in the cloud. 2. Build: retrieve docs → prompt → output. 3. Export / hand off to eng when the flow proves value.
Example prompts
- “Build a Langflow RAG flow over our help center.”
- “When should we graduate from Langflow to code?”
- “Add a tool node that calls our HTTP API.”
Use cases and examples
- Evaluate langflow-ai/langflow for your stack
- Onboard a teammate to Langflow with a shared checklist
- Compare Langflow against tools you already pay for
- Capture lessons in your internal wiki after a pilot
Prerequisites
- Docker or supported install
- LLM API keys
Tips
- Prototype here; productionize with tests and observability.
- Version your flows.