Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
7b85505117* 📝 (loop.py): Improve code readability and semantics by updating the return condition in the LoopComponent class 📝 (data.py): Enhance the data_to_text_list function to handle various data structures and ensure consistent text formatting 📝 (LoopTemplate.json): Update the LoopComponent class description and method implementations for better clarity and maintainability 📝 (test_data_to_text_list.py): Add unit tests to cover different scenarios and edge cases for the data_to_text_list function * [autofix.ci] apply automated fixes * 📝 (langflow): Add noqa comments to ignore linting rule A005 for specific files in the project. Fix type annotations and improve error handling in data.py module. Add noqa comments to ignore linting rule A005 for specific files in the project. * ✅ (test_data_to_text_list.py): update parameter names in pytest.mark.parametrize to improve readability and maintainability * 🐛 (data.py): refactor error message handling in data_to_text_list function to improve readability and maintainability * [autofix.ci] apply automated fixes * Update src/backend/base/langflow/helpers/data.py Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> * ♻️ (data.py): refactor data_to_text_list function to simplify the logic and improve readability by removing unnecessary if conditions and updating the format_dict directly from data_obj.data. * ✅ (test_data.py): refactor test_data_to_text_list function to handle defaultdict behavior and improve test coverage * ✅ (loop-component.spec.ts): update test expectation to match the correct number of occurrences of "modified_value" in the output * ✅ (Memory Chatbot.spec.ts): add assertion to check if the concatenated text length is greater than 20 to ensure the chatbot response is meaningful --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> |
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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.