f5ba8f54ca
* fix: handle Windows path separators in Policies ToolGuard Guard mode (#13727) Guard mode read generated guard files back from the node template with str(file_name), where file_name is a pathlib.Path from the toolguard result model. On Windows str() yields backslashes, but the files are stored (by sync_generated_guard_code_inputs) under their POSIX relative path via Path.as_posix() (forward slashes). Every lookup therefore missed on Windows, attrs.get(...) returned None, and None["value"] crashed with "'NoneType' object is not subscriptable". - Add PoliciesComponent._template_field_key() to normalize a file name to the POSIX key the sync step writes; route all reads in make_toolguard_result() through it so lookups match on every platform. - Raise a clear "re-run Generate" error when a generated field is missing instead of subscripting None. - Relax validate_before_generate(): api_key is optional (required=False, advanced=True) and credentials can come from the model connection/env, so only the model selection is required. Fixes the spurious "model or api_key cannot be empty!" block. - Regenerate the bundled component index for the updated source. The separate "Generate emits the pass # FIXME stub" and Windows charmap-decode defects are in the upstream toolguard package (latest 0.2.19) and are out of scope here; this component is ready to consume a fixed toolguard release once available. Fixes #13727 * [autofix.ci] apply automated fixes * Update templates * [autofix.ci] apply automated fixes * fix: address review feedback on Policies component - Add `str | Path` type hints to `_template_field_key` and the inner `read_content` helper (mypy compliance). - Give the empty-template `ValueError` in `make_toolguard_result` a descriptive message instead of a bare raise. - Regenerate the bundled component index for the updated source. * Update component_index.json * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Langflow is a powerful platform 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.
🖥️ Langflow Desktop
Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don't need to manage Python environments or install packages manually. Available for Windows and macOS.
⚡️ Quickstart
Install locally (recommended)
Requires Python 3.10–3.14 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install langflow -U
The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.
Run
To start Langflow, run:
uv run langflow run
Langflow starts at http://127.0.0.1:7860.
That's it! You're ready to build with Langflow! 🎉
📦 Other install options
Run from source
If you've cloned this repository and want to contribute, run this command from the repository root:
make run_cli
For more information, see DEVELOPMENT.md.
Docker
Start a Langflow container with default settings:
docker run -p 7860:7860 langflowai/langflow:latest
Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.
🛡️ Security
For security information, see our Security Policy.
🚀 Deployment
Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.
⭐ 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.