Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
360836800d* Create convertor.py * [autofix.ci] apply automated fixes * convert component * [autofix.ci] apply automated fixes * add Type_conversion base class with dispatchers for performance based conversion * fix lint issues * add type_convertor test * [autofix.ci] apply automated fixes * update tests * fix tests * update with auto conversion methods * update function to component file * feat: enhance input validation for Data, DataFrame, and Message types * test: add unit tests for DataInput, MessageInput, and DataFrameInput data conversion * updated changes to use type classes * [autofix.ci] apply automated fixes * add convert logic * update converts * Update converter.py * [autofix.ci] apply automated fixes * revert converter.py * Update inputs.py * Update test_inputs.py * update to logic * Update test_type_convertor_component.py * update converter * [autofix.ci] apply automated fixes * refactor: rename conversion functions for clarity Updated function names for converting inputs to Message, Data, and DataFrame types to improve readability and consistency. The changes include renaming `get_message_converter` to `convert_to_message`, `get_data_converter` to `convert_to_data`, and `get_dataframe_converter` to `convert_to_dataframe`. Additionally, added a check for dictionary input in the data conversion function. * fix: add TYPE_CHECKING for conditional imports in message.py Introduced TYPE_CHECKING to optimize imports for the DataFrame type, ensuring that the import only occurs during type checking. This change enhances performance and maintains compatibility with static type checkers. * refactor: simplify data conversion methods in Message class Removed unnecessary parameters from the `to_data` and `to_dataframe` methods in the Message class, enhancing clarity and reducing complexity. The methods now directly use instance attributes, improving code readability and maintainability. * refactor: enhance DataFrame methods for clarity and type safety Updated the `to_data` and `to_message` methods in the DataFrame class to improve clarity and type safety. The `to_data` method now directly converts the DataFrame to a Data object without parameters, and the `to_message` method uses the instance's data directly. Added TYPE_CHECKING for conditional imports to optimize performance and maintain compatibility with static type checkers. * refactor: streamline Data class methods for improved clarity Refactored the `to_message` and `to_dataframe` methods in the Data class to eliminate unnecessary parameters and directly utilize instance attributes. This change enhances code readability and maintainability while ensuring type safety with the appropriate imports for Message and DataFrame. Additionally, updated the logic to access instance data more intuitively. * refactor: simplify conversion method calls by removing redundant arguments * rename test file * refactor: remove obsolete test file for data conversion * refactor: add support for converting dictionary to DataFrame --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> Co-authored-by: Carlos Coelho <80289056+carlosrcoelho@users.noreply.github.com> Co-authored-by: Yuqi Tang <yuqi.tang@datastax.com> |
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Langflow is a powerful tool for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and a built-in API server that turns every agent into an API endpoint 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 to get started quickly and iterate.
- Access to Code so developers can tweak any component using Python.
- Playground to immediately test and iterate on their flows with step-by-step control.
- Multi-agent orchestration and conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
⚡️ Quickstart
Langflow works with Python 3.10 to 3.13.
Install with uv (recommended)
uv pip install langflow
Install with pip
pip install langflow
📦 Deployment
Self-managed
Langflow is completely open source and you can deploy it to all major deployment clouds. Follow this guide to learn how to use Docker to deploy Langflow.
Fully-managed by DataStax
DataStax Langflow is a full-managed environment with zero setup. Developers can sign up for a free account to get started.
⭐ 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.