305a85a0fb
LoopComponent only declared input_types=["DataFrame", "Table"] on its `data` handle, even though the class is documented to iterate over Data or Message objects and ships a `_convert_message_to_data` helper. Any Message-producing component (ChatInput, Agent, ...) was therefore rejected at connect time, which made agent-assisted flow builders retry until they exhausted the LangGraph recursion limit and crashed. Changes: - Add "Data" and "Message" to the `data` handle input_types (covers Data/JSON via TYPE_MIGRATIONS) and clarify the info text. - Declare explicit output types: item -> ["Data"], done -> ["DataFrame", "Table"], so describe_component and the registry surface type metadata for downstream planning. - Convert Message inputs to a clean Data object in `_validate_data` before validation. Message subclasses Data, so without this it was accepted verbatim as a single Data carrying the whole message envelope instead of its payload. - Regenerate the component index, the "Research Translation Loop" starter project, and the en.json locale string to match. Add lfx regression tests covering the input/output type metadata, the connect-time compatibility check, and the Message -> Data conversion.
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 3.10–3.14 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.