b7513e5c6f* feat: Add smoke tests workflow for PR validation - Adds smoke-test label trigger for running tests without API keys - Runs backend tests excluding api_key_required markers - Runs full frontend test suite with mocked APIs - Provides fast feedback (~10-15 min) before lgtm label - Enables external contributors to validate changes without API costs - Comments results back to PR automatically Usage: Add 'smoke-test' label to any PR to trigger 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: Fix YAML indentation in smoke-tests workflow script block * refactor: Focus smoke tests on critical functionality only - Run only 10 essential backend test files (version, schema, serialization, etc.) - Run only frontend unit tests (skip integration/e2e) - Reduce timeout from 15 to 8 minutes - Target critical functionality without external dependencies - Avoid problematic database/API-dependent tests * fix: Use correct Jest CLI option --testPathPatterns * add frontend tests * add tests * change to essential tests * fix: Create superuser in test_load_bundles_from_urls test The test_load_bundles_from_urls test was failing because the load_bundles_from_urls function expects a superuser to exist in the database, but the test client fixture sets LANGFLOW_AUTO_LOGIN=false by default, which doesn't create a superuser. This fix creates a superuser in the test database before calling the function, resolving the 'Superuser not found in the database' error. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * add on conditions * fix: Check PR labels correctly for smoke test trigger Change condition from github.event.label.name to github.event.pull_request.labels.*.name to work with all trigger events (synchronize, opened, etc), not just labeled events. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * add workdispatch * remove initial test * adding timeout minutes * edit env var --------- Co-authored-by: Claude <noreply@anthropic.com> |
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| CODE_OF_CONDUCT.md | ||
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| CONTRIBUTING.md | ||
| DEVELOPMENT.md | ||
| LICENSE | ||
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| README.md | ||
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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.