Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
http://www.langflow.org
7aeb687533* fix: update message input handling in LCAgentComponent and improve memory data retrieval * Refactor MessageTextInput to MessageInput for consistency. * Enhance input dictionary construction to handle different input types in LCAgentComponent. * Update get_memory_data method to filter out current input value from retrieved messages. * fix: update AgentComponent to include documentation link and improve input handling * Added documentation link for AgentComponent. * Removed memory inputs from the agent component for cleaner input management. * Enhanced error handling in message_response method to ensure better validation and logging of exceptions. * fix: enhance input handling in LCAgentComponent by updating message conversion * Updated input dictionary construction in LCAgentComponent to use to_lc_message() for Message instances, improving input handling consistency. * test: add regression test for message duplication in agent component * Introduced a new test to verify that mathematical expressions do not experience message duplication when processed by the agent component. * The test checks both input and output JSON to ensure correct handling of expressions like "2+2" without duplication errors. * test: add workspace tag to regression test for message duplication in agent component * Updated the regression test for mathematical expressions to include the "@workspace" tag, enhancing test categorization and organization. * This change ensures better tracking and management of tests related to the agent component. * fix: add temporary comment in get_memory_data to address message duplication * Added a TODO comment in the get_memory_data method of AgentComponent to indicate a temporary fix for message duplication issues. This serves as a reminder to develop a more robust solution in the future. * feat: add message extraction utility for BaseMessage * Introduced a new helper function, _get_message_from_base_message, to extract and concatenate text content from BaseMessage instances, improving message handling. * Updated input handling in handle_on_chain_start to utilize the new extraction function, ensuring consistent processing of input messages. * refactor: standardize code snippets across starter project JSON files * Updated the "value" field in multiple starter project JSON files to ensure consistent formatting and structure of code snippets. * This change enhances readability and maintainability of the code examples provided in the starter projects. * feat: add caching and content dictionary creation for images * Introduced a new function, create_image_content_dict, to generate a content dictionary for multimodal inputs from image files, enhancing image handling capabilities. * Implemented LRU caching to optimize performance for repeated image processing. * Added comprehensive error handling and documentation for better usability and maintainability. * refactor: update message handling to utilize create_image_content_dict * Replaced direct image URL creation with create_image_content_dict for improved image content handling in the Data and Message classes. * Adjusted the order of content in human messages to ensure text appears first, enhancing message structure and clarity. * Removed deprecated to_lc_message method to streamline the codebase and improve maintainability. * docs: enhance _get_message_from_base_message docstring for clarity * Expanded the docstring for the _get_message_from_base_message function to provide detailed information on input types, expected behavior, and examples of usage. * Improved documentation aims to enhance usability and maintainability of the code by clarifying how to extract text content from BaseMessage instances. * refactor: enhance image path handling and update message content structure * Modified the get_file_paths function to support both Image objects and string paths for improved flexibility in file handling. * Updated test cases to reflect changes in image content structure, ensuring consistency in type and source type attributes. * Introduced new tests for create_image_content_dict to validate successful creation and error handling for image content dictionaries. * refactor: streamline message extraction in handle_on_chain_start * Removed the _get_message_from_base_message function to simplify the codebase. * Updated handle_on_chain_start to directly use the text method of BaseMessage for extracting message content, enhancing clarity and maintainability. * feat: enhance input handling for multimodal messages * Added functionality to process image content within input messages, ensuring images are included in chat history as HumanMessage instances. * Updated input handling logic to separate image types from text, improving the structure and clarity of message content. * This enhancement supports better management of multimodal inputs in the agent's chat history. * feat: add to_lc_message method for converting Data to BaseMessage * Introduced the to_lc_message method in the Message class to facilitate conversion of Data instances to BaseMessage. * Implemented logic to handle both HumanMessage and AIMessage based on the presence of required keys and sender type. * Added logging for missing required keys to improve debugging and maintainability. * refactor: simplify sender check in Message class * Updated the sender validation logic in the Message class to remove unnecessary checks for missing sender values. * This change enhances code clarity and maintains the intended functionality for handling user messages with associated files. * test: update test_message_from_human_text to reflect content type change * Modified the test for message conversion to assert that lc_message.content is a string instead of a list. * Updated assertions to ensure the content matches the expected text, enhancing test accuracy and reliability. * fix: update sender validation in Message class and adjust test case * Modified the sender validation logic to handle cases where the sender is not specified, defaulting to HumanMessage. * Updated the corresponding test case to reflect this change, ensuring accurate type assertion for lc_message when no sender is provided. * refactor: update import statements for consistency and clarity * Replaced the import of BaseModel from langchain.pydantic_v1 with the direct import from pydantic to streamline dependencies. * This change enhances code clarity and aligns with best practices for managing imports in the codebase. --------- Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: Carlos Coelho <80289056+carlosrcoelho@users.noreply.github.com> |
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| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| DEVELOPMENT.md | ||
| eslint.config.js | ||
| LICENSE | ||
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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.