f7e99ee9b6
Follow-up cleanup on top of the badge + toast change. Addresses the
B-grade items I called out in self-review:
- Extract MemoryAutoCaptureToggle component. MemoryDetailsHeader was
inlining a 20-line Tailwind blob; pull it into a small presentational
component with prop-level JSDoc and a single accessible button.
Header now reads as orchestration only.
- Replace the hand-rolled status pill with the existing Badge primitive
(variant=successStatic / secondaryStatic, size=sm). One source of
truth for badge tokens across the app — no more drifting Tailwind
blobs.
- Route all user-visible Auto-capture copy through i18n
(memories.autoCapture.{label,stateOn,stateOff,ariaEnable,ariaDisable,
toastEnabled,toastDisabled} in en.json). No more hardcoded English
in TS.
- Export NextIsActive from useAutoCaptureDebouncedToggle so the
toggle component can type its onToggle prop precisely.
- Add focused unit tests for useAutoCaptureDebouncedToggle in
isolation (debounce, on→off→on collapse, success/error callbacks,
memory-id cleanup, no-memory no-op). Previously only covered through
useMemoriesData integration tests.
- Update MemoryDetailsHeader.test.tsx to assert against the new
state-aware aria-labels and the badge testid.
74/74 memories suite green, 841/841 across FlowPage + createMemoryModal.
Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
✨ Highlight features
- Visual builder interface to quickly get started and iterate.
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Deploy as an MCP server and turn your flows into tools for MCP clients.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
🖥️ Langflow Desktop
Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don't need to manage Python environments or install packages manually. Available for Windows and macOS.
⚡️ Quickstart
Install locally (recommended)
Requires Python 3.10–3.13 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install langflow -U
The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.
Run
To start Langflow, run:
uv run langflow run
Langflow starts at http://127.0.0.1:7860.
That's it! You're ready to build with Langflow! 🎉
📦 Other install options
Run from source
If you've cloned this repository and want to contribute, run this command from the repository root:
make run_cli
For more information, see DEVELOPMENT.md.
Docker
Start a Langflow container with default settings:
docker run -p 7860:7860 langflowai/langflow:latest
Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.
🛡️ Security
For security information, see our Security Policy.
🚀 Deployment
Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.
⭐ Stay up-to-date
Star Langflow on GitHub to be instantly notified of new releases.
👋 Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.