Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
bc6e918f49* Add cycle detection and handling in graph edge building process - Introduced `cycles` property to detect cycles in the graph. - Modified `_build_edges` and `build_edge` methods to differentiate between `CycleEdge` and `Edge`. - Updated imports and type hints to support new functionality. * Add cycle detection and handling in graph processing - Introduced `is_cyclic` property to check for cycles in the graph. - Added `_snapshot` method for capturing the current state of the graph. - Modified `layered_topological_sort` to handle cyclic graphs by starting from a specified start component. - Updated imports and type hints for better code clarity and functionality. * Refactor tests and components for improved caching and data handling - Updated `test_vector_store_rag.py` to use `set_on_output` with `cache=True` and simplified assertions. - Enhanced `test_memory_chatbot.py` with additional assertions for graph structure and caching. - Simplified `to_data` method in `base.py` to directly return `_data` without JSON serialization. * Add unit tests for detecting cycles in graph - Introduce `test_cycle_in_graph` to verify cyclic behavior in the graph. - Add `test_cycle_in_graph_max_iterations` to ensure max iterations limit is respected. - Implement `Concatenate` component for testing purposes. * Disable output cache in graph tests to allow loops to work * Refactor: Update VertexStates enum values to uppercase and optimize imports in base.py * Refactor type hints and improve error handling in `Vertex` class - Replace `ValueError` with `NoComponentInstance` exception for missing component instances. - Add `target_handle_name` parameter to `_get_result` method for better result retrieval. - Refactor type hints to use `collections.abc` for `AsyncIterator`, `Generator`, and `Iterator`. - Update type hints for `extract_messages_from_artifacts` and `successors_ids` methods to use generic `dict` and `list`. |
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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 pip (Python 3.10 or greater):
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.