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* fix(authz): schedule periodic audit-log retention cleanup clean_authz_audit_log() was only invoked once, at startup inside initialize_services(). A long-running instance never pruned authz_audit_log again after boot, so the table grew unbounded between restarts even though the retention helper was already implemented and unit-tested. Wire the same helper to a recurring background worker: - New AuditLogCleanupWorker (services/task/audit_cleanup.py), modelled on the sibling temp_flow_cleanup.CleanupWorker: a stop-event-driven asyncio task that prunes on a fixed interval in its own session_scope(). Best-effort -- the helper logs-and-swallows DB errors and an outer guard keeps the loop alive, so it never blocks the event loop or request path. - Gated: the worker only schedules when AUTHZ_AUDIT_ENABLED is True and AUTHZ_AUDIT_RETENTION_DAYS > 0; otherwise start() is a no-op. Retention=0 stays a no-op end to end. - Sleep-first loop: the unconditional startup sweep still prunes at boot, so the first scheduled pass waits one interval to avoid a redundant immediate delete. The startup sweep is left unchanged. - New AUTHZ_AUDIT_CLEANUP_INTERVAL setting (default 86400 = daily, floor 300s). - Started/stopped from the application lifespan, both best-effort. Tests (test_audit_cleanup_worker.py) show cleanup runs repeatedly on the recurring schedule (not just at startup), is a no-op when retention or auditing is disabled, survives sweep failures, and -- end to end against a real SQLite engine -- prunes a row inserted after startup while leaving in-window rows. Closes the audit-retention-scheduling gap (C) for OSS RBAC on release-1.10.0. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * refactor(authz): read AUTHZ_AUDIT_CLEANUP_INTERVAL directly The cleanup worker resolved the sweep interval via getattr-with-default plus a TypeError/ValueError guard, mirroring the field default in a module constant. Pydantic already guarantees the field is present and >= 300, so those fallbacks were unreachable and the constant was a second source of truth that could drift. Read the setting directly; keep DEFAULT_CLEANUP_INTERVAL_SECONDS only as the pre-start() placeholder for unstarted workers. Drop the matching missing/garbage-fallback test assertion. --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.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.