Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
2be7c56939* Add utility functions to build Pydantic models from schema definitions * Add unit tests for build_model_from_schema function in test_base_model.py - Implement various test cases to validate the functionality of build_model_from_schema. - Test cases cover scenarios such as handling valid and empty schemas, managing unknown field types, and processing schemas with missing optional keys. - Ensure proper handling of nested list and dict types, and verify the function's efficiency with large schemas. - Confirm that the function raises exceptions for invalid input and handles duplicate field names correctly. * Refactor tests in `test_base_model.py` to improve type handling and error checking * Refactor output schema handling to use TableInput and build_model_from_schema * Update OpenAI model components and hierarchical crew setup - Refactor `OpenAIModelComponent` to use `TableInput` for `output_schema` and integrate `build_model_from_schema`. - Modify `HierarchicalCrewComponent` to use unpacking for base inputs. - Ensure consistent import statements across JSON files. - Improve error handling and logging for vector store operations. * Add chat result model with message building and execution logic - Implement `build_messages_and_runnable` to construct message lists and configure runnable models. - Add `get_chat_result` to execute language models with input messages, supporting streaming and custom configurations. - Handle exceptions with optional custom error messages. * Add "table" to DIRECT_TYPES in constants.py * Add support for DataFrame input validation in TableInput class * Add StructuredOutputComponent for generating structured outputs from language models * Enhance structured output component with improved input descriptions and schema naming * Convert DataFrame to list of dictionaries in TableInput validation * Remove pandas dependency and refactor schema handling in structured_output.py * Remove 'default' field from structured output schema and update field initialization * Add 'number' and 'text' types to type mapping and remove default value from field creation * Enhance error handling in structured output building process * Improve error message for non-BaseModel output in structured_output.py * Add unit tests for StructuredOutputComponent in helpers module - Implement various test cases to ensure correct functionality of StructuredOutputComponent. - Test successful structured output generation, handling of unsupported language models, and correct output model building. - Validate handling of multiple outputs, empty and invalid output schemas, and nested schemas. - Include tests for large input values and invalid language model configurations. * Update description for StructuredOutputComponent to clarify functionality * Add default values and error handling for structured output in helpers * Remove unused 'method' parameter from 'with_structured_output' in MockLanguageModel * refactor: rename test_base_model.py to test_base_model_from_schema.py Rename the test_base_model.py file to test_base_model_from_schema.py to better reflect its purpose of testing the build_model_from_schema function. This change improves code clarity and maintainability. * Add type ignore comments to suppress type checking errors * Add Generic typing to StructuredOutputComponent and fix method call * Revert "Refactor output schema handling to use TableInput and build_model_from_schema" This reverts commit 2e84a8608689bcfb519dc589d3eeef852784f3e4. * Deprecate JSON mode in OpenAIModel output schema documentation * Remove unused Generic import and add type ignore comment in StructuredOutputComponent * Refactor OpenAI model components and deprecate output schema - Refactored `OpenAIModelComponent` to use `operator.ior` and `functools.reduce` for converting `output_schema` to a dictionary. - Deprecated the `output_schema` field, updating its info to reflect the deprecation. - Simplified the `_docs_to_data` method in `SplitTextComponent` for better readability. - Updated import statements and removed unused imports across multiple JSON files. * Add specific type ignore comments and update exception types in backend code |
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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.