46ba809dae
* feat(telemetry): Create a new SCARF telemetry event to track the registered email address - Temporarily add hardcoded registered email address to common SCARF telemetry events - Only send registered email address if it's defined and the context is Langflow Desktop - Implement a utility to load and fetch the registered email address - Bootstrap common telemetry fields with the registered email address (only in the Langflow Desktop context) - Lazy load registered email address - Cache loaded registered email address - Adopt latest email registration storage format / schema - Create a new telemetry schema for the registered email address - Implement utility methods to send new telemetry event for the registered email address - Send new telemetry event for the registered email address on telemetry service start-up lifecycle method - Code clean-up, commenting & refactoring - Implement 4 new test scenarios for testing the new Email payload schema - Fix all pre-existing style errors - Implement 6 new test scenarios for testing the "get email model" function from the registered email utility that invokes the registration API endpoint and caches the result * feat(telemetry): Create a new SCARF telemetry event to track the registered email address - Address PR feedback from Co-pilot
Caution
- Langflow versions 1.6.0 through 1.6.3 have a critical bug where
.envfiles are not read, potentially causing security vulnerabilities. DO NOT upgrade to these versions if you use.envfiles for configuration. Instead, upgrade to 1.6.4, which includes a fix for this bug.- Windows users of Langflow Desktop should not use the in-app update feature to upgrade to Langflow version 1.6.0. For upgrade instructions, see Windows Desktop update issue.
- Users must update to Langflow >= 1.3 to protect against CVE-2025-3248
- Users must update to Langflow >= 1.5.1 to protect against CVE-2025-57760
For security information, see our Security Policy and Security Advisories.
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
Install locally (recommended)
Requires Python 3.10–3.13 and uv (recommended package manager).
Install
uv pip install langflow -U
Installs the latest Langflow package.
Run
uv run langflow run
Starts the Langflow server at http://127.0.0.1:7860.
That's it! You're ready to build with Langflow 🎉
Other install options
Install from repo
If you're contributing or running from source, see DEVELOPMENT.md for setup instructions.
📦 Deployment
Langflow is completely open source, and you can deploy it to all major 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.