a0e484c0eb* feat: add DataFrame output to Structured Output component - Add new DataFrame output alongside existing Data output for structured data - Single output: creates DataFrame with one row - Multiple outputs: creates DataFrame with multiple rows, each containing a Data object - Maintains backward compatibility with existing Data output - Includes comprehensive test coverage for both single and multiple output scenarios 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * [autofix.ci] apply automated fixes * Update structured_output.py * [autofix.ci] apply automated fixes * udpate to tests * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * update to format instructions * Update system prompt in structured output component test Expanded the system prompt in the test for StructuredOutputComponent to provide more detailed extraction instructions, including handling of missing values, duplicates, and output format requirements. This improves test clarity and better simulates real-world extraction scenarios. * [autofix.ci] apply automated fixes * Update structured_output.py * fix: return empty Data object instead of None in StructuredOutputComponent Updated the return statement in the StructuredOutputComponent to return an empty Data object when there are no outputs, improving consistency in the output handling. * [autofix.ci] apply automated fixes --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> |
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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.