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dd68a97567
* fix: update pre-commit configuration for ruff formatting args * fix: update variable type constant from GENERIC_TYPE to CREDENTIAL_TYPE * feat: enhance variable service to handle decryption for generic type and update default variable type to CREDENTIAL_TYPE * feat: add abstract method to retrieve all variables for a user in VariableService * feat: implement get_all method in KubernetesSecretService to retrieve and decrypt user variables * refactor: update variable tests to use fixtures for generic and credential types - Renamed and refactored test fixtures for better clarity and reusability. - Updated tests to utilize `generic_variable` and `credential_variable` fixtures instead of hardcoded values. - Enhanced assertions to ensure correct handling of variable types, including encryption for credential variables and decryption for generic variables. - Improved test structure for creating, reading, updating, and deleting variables, ensuring consistency across test cases. * refactor: update get_all method signatures in variable services to return VariableRead - Changed return type of the get_all method in VariableService, KubernetesSecretService, and DatabaseVariableService from list[Variable | None] to list[VariableRead]. - This update enhances type consistency across variable services and aligns with the new VariableRead model for improved data handling. * fix: update variable type assertion in test_update_variable to CREDENTIAL_TYPE - Changed the assertion in the `test_update_variable` test to verify that the result type is now `CREDENTIAL_TYPE` instead of `GENERIC_TYPE`. - This update aligns the test with recent changes in variable type handling, ensuring accurate validation of variable updates. * fix: update variable type assertion in test_create_variable to CREDENTIAL_TYPE - Changed the assertion in the `test_create_variable` test to verify that the result type is now `CREDENTIAL_TYPE` instead of `GENERIC_TYPE`. - This update ensures consistency with recent changes in variable type handling and improves the accuracy of the test validation.
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
Docs - Free Cloud Service - Self Managed
✨ Core features
- Python-based and agnostic to models, APIs, data sources, or databases.
- Visual IDE for drag-and-drop building and testing of workflows.
- Playground to immediately test and iterate workflows with step-by-step control.
- Multi-agent orchestration and conversation management and retrieval.
- Free cloud service to get started in minutes with no setup.
- Publish as an API or export as a Python application.
- Observability with LangSmith, LangFuse, or LangWatch integration.
- Enterprise-grade security and scalability with free DataStax Langflow cloud service.
- Customize workflows or create flows entirely just using Python.
- Ecosystem integrations as reusable components for any model, API or database.
📦 Quickstart
- Install with uv (recommended) (Python 3.10 to 3.12):
uv pip install langflow
- Install with pip (Python 3.10 to 3.12):
pip install langflow
- Cloud: DataStax Langflow is a hosted environment with zero setup. Sign up for a free account.
- Self-managed: Run Langflow in your environment. Install Langflow to run a local Langflow server, and then use the Quickstart guide to create and execute a flow.
- Hugging Face: Clone the space using this link to create a Langflow workspace.
⭐ 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.