Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
2c405f77e7* refactor: update _generate_code_hash function and enhance module name handling - Removed the class_name parameter from _generate_code_hash for improved clarity and simplicity. - Added a new function, get_module_name_from_display_name, to generate module names from display names in snake_case. - Updated build_custom_component_template_from_inputs to use the new module name generation logic when module_name is None. - Enhanced error handling in code hash generation to log exceptions appropriately. - Updated unit tests to reflect changes in the _generate_code_hash function and to verify the new module name generation functionality. * fix: enhance module name handling and error logging in build_custom_component_template - Added logic to derive module names from display names when not provided, improving metadata accuracy. - Refined error handling for code hash generation, ensuring exceptions are logged appropriately for better debugging. * test: add comprehensive unit tests for metadata generation in custom components - Introduced multiple tests to ensure that the `build_custom_component_template` function consistently generates metadata, including module names and code hashes, across various scenarios. - Verified that metadata is correctly returned when module names are provided or omitted, and that code hashes change with component code modifications. - Included tests for handling unicode characters in component code to ensure robustness in metadata generation. * test: update unit tests to use Component class for metadata generation - Refactored test cases to replace the CustomComponent with the new Component class, ensuring consistency in testing metadata addition in template builders. - Adjusted mock component attributes to align with the updated class structure, enhancing clarity and maintainability of the tests. * test: add unit tests for custom component metadata retrieval and consistency - Introduced new tests for the /custom_component endpoint to verify that it returns accurate metadata, including module names and code hashes. - Ensured that identical component code produces consistent metadata across multiple requests, enhancing the reliability of the custom component functionality. * refactor: improve error logging in code hash generation - Updated error logging in `build_custom_component_template_from_inputs` and `build_custom_component_template` to use debug level with exception context, enhancing clarity for debugging while reducing log noise. - This change aims to provide more detailed insights during error occurrences without cluttering the error logs. |
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Caution
Users must update to Langflow >= 1.3 to protect against CVE-2025-3248.
Langflow is a powerful tool for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
✨ Highlight features
- Visual builder interface to quickly get started and iterate .
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Deploy as an MCP server and turn your flows into tools for MCP clients.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
⚡️ Quickstart
Langflow requires Python 3.10 to 3.13 and uv.
- To install Langflow, run:
uv pip install langflow -U
- To run Langflow, run:
uv run langflow run
- Go to the default Langflow URL at
http://127.0.0.1:7860.
For more information about installing Langflow, including Docker and Desktop options, see Install Langflow.
📦 Deployment
Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to use Docker to deploy Langflow, see the Docker deployment guide.
⭐ 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.