Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
4221fa40e6* Set `_has_cycle_edges` to `True` for source and target vertices in cycle edges * feat: Add `has_cycle_edges` method to Vertex class The `has_cycle_edges` method is added to the `Vertex` class to check if the vertex has any cycle edges. Additionally, the `instantiate_component` method is updated to use the `initialize.loading.instantiate_class` function for custom component instantiation. * Add `apply_on_outputs` method to Vertex for applying functions to outputs * Add utility to find vertices in cycles within a directed graph - Implement `find_cycle_vertices` function to identify all vertices that are part of cycles in a directed graph. - Utilize depth-first search (DFS) to detect cycles and collect vertices involved in those cycles. * Add unit tests for `find_cycle_vertices` utility function in graph module * Add method to set cache for vertices in cycle - Introduced `_set_cache_to_vertices_in_cycle` method to enable caching for vertices involved in cycles. - Added `find_cycle_vertices` import to support the new method. - Refactored vertex instantiation into `_instantiate_components_in_vertices` method for better code organization. * refactor: Update caching logic for vertices in cycles Refactor the `_set_cache_to_vertices_in_cycle` method to improve caching logic for vertices involved in cycles. Instead of setting the `cache` attribute to `True`, it is now set to `False` for better clarity and consistency. This change ensures that the cache is properly handled for vertices in cycles. * Refactor `find_cycle_vertices` to use NetworkX for cycle detection * Refactor `find_cycle_vertices` tests to remove entry point parameter and add new test case - Removed the `entry_point` parameter from all test cases for `find_cycle_vertices`. - Added a new parameterized test case `test_handle_two_inputs_in_cycle` to verify handling of cycles with two inputs. * Disable cache in cycle: Update `apply_on_outputs` to handle empty outputs in `base.py` * Add unit test to ensure output cache is disabled in graph cycles * Add unit test for graph cyclicity with prompt components and OpenAI integration - Introduce `test_updated_graph_with_prompts` to validate graph cyclicity and execution. - Integrate `PromptComponent`, `OpenAIModelComponent`, and `ConditionalRouterComponent` in the test. - Ensure graph execution with a maximum of 20 iterations and cache disabled. - Validate the presence of expected output vertices in the results. * Convert `_instantiate_components_in_vertices` to async and disable cache in cycle vertices * Add default value handling for cycle edges in vertex component - Introduced `default_value` to handle cases where edges are cycles and target parameters are present. - Ensured that `default_value` is returned if defined, preventing errors when the component is not built. * Switch from os.environ to os.getenv for API key retrieval in test_cycles.py * Add __repr__ method to Edge class to indicate cycle edges with a symbol * Refactor test_cycles.py to streamline component initialization and update assertions - Simplified component initialization using method chaining. - Corrected router input and message parameters to use openai_component_1. - Updated assertions to check for correct output IDs. * Refactor test_cycles.py to streamline component initialization and update assertions * Refactor test to use custom serialization method instead of pickle * Add cycle_vertices property to optimize cycle detection in graph - Introduced `_cycle_vertices` attribute to store vertices involved in cycles. - Added `cycle_vertices` property to compute and cache cycle vertices. - Updated edge creation logic to use `cycle_vertices` for cycle detection. * Enhance error message in `types.py` to include component ID for better debugging * Refactor test_cycles.py to update graph configuration and assertions - Changed router operator from "equals" to "contains". - Consolidated chat output to a single component. - Updated graph construction to use a single chat output. - Replaced `_snapshot` with `get_snapshot` for graph state capture. - Adjusted assertions to reflect the updated graph structure and outputs. * Add api_key_required marker to test_updated_graph_with_prompts test * Add validation to require max_iterations for cyclic graphs * run ruff - Refactored error message handling in `base.py` for cyclic graphs. - Optimized cycle vertex extraction in `utils.py` by using set comprehension. * Comment out tests for loading flow from JSON in test_loading.py * Refactor test fixture for webhook flow creation in conftest.py * Update unit tests to reflect new webhook flow structure in vertices endpoints * Temporarily disable tests for loading Langchain objects with and without cached sessions * Disable caching in vector store and OpenAI model components |
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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.