e136bc7e70
SQLModel's AsyncSession raises a DeprecationWarning on every
session.execute() call urging the caller to use session.exec() —
but exec is typed for SELECT-returning statements only. Our five
execute() call-sites all pass non-SELECT statements that exec
explicitly does not accept (text() raw SQL or update() constructs),
so the warning is informational and cannot be acted on by changing
the code.
In the worker's hot path the warning fires on every claim cycle
(every 0.5–5s depending on backoff), flooding the log. The crud
helpers fire it once at boot (reset_stalled_in_progress) and once
per flow save (cancel_queued_jobs_for_components). None are
actionable, all are noisy.
NEW services/triggers/_sqlmodel_compat.py
suppress_sqlmodel_exec_warning() — a narrow context manager that
filters DeprecationWarning ONLY from sqlmodel.ext.asyncio.session.
Any other DeprecationWarning still surfaces. The module is
underscore-prefixed to signal it's an internal compatibility shim,
not part of the public surface.
Five call-sites wrapped:
worker.py (3): _claim_one's Postgres FOR UPDATE SKIP LOCKED select,
its follow-up UPDATE, and the SQLite optimistic
UPDATE...RETURNING.
crud.py (2): cancel_queued_jobs_for_components and
reset_stalled_in_progress, both update() constructs.
Verified by running the full trigger test suite with
-W 'error::DeprecationWarning:sqlmodel.ext.asyncio.session' — all
tests still pass, confirming the suppression actually engages at
runtime (without it, the warnings-as-errors filter would fail every
test that hits the worker path). Ruff clean on all three files.
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 3.10–3.13 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.
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