4b0644f577
Address review feedback on the OSS authorization foundations PR:
- B1/I1: Split services/authorization/utils.py (694 lines, mixed
responsibilities) into focused modules:
- audit.py — batched audit pipeline (audit_decision, writer loop)
- guards.py — ensure_*_permission family (collapsed via _RESOURCE_SPECS
registry; nine 25-line clones become 7 thin wrappers)
- listing.py — filter_visible_resources
utils.py remains as a back-compat re-export shim so existing call sites
(api/v1/flows.py, deployments.py, etc.) keep working untouched.
- B2: Split tests/unit/services/authorization/test_utils.py (1004 lines)
into test_audit.py, test_guards.py, test_filter_visible.py,
test_domain_resolution.py. Shared stubs + monkeypatch helpers extracted
into _common.py; pytest fixtures into conftest.py.
- I2: ensure_permission default 403 detail is now "Permission denied" —
callers can opt into a richer message via the new `detail=` kwarg. The
previous default echoed flow:<uuid> in the response, leaking existence
to non-owners on any route that forgot to wrap in deny_to_404.
- I4: Document the OSS floor contract on _ensure_can_administer_share —
the early-return is dead in OSS (supports_cross_user_fetch() is False)
and the explicit 403 is the actual floor; the enterprise plugin gates
via the downstream ensure_share_permission call.
- R2: Audit drop logging is now time-based (first drop + at most every
10s during persistent saturation) instead of every 1000th drop. Low
drop rates no longer go minutes without a log line.
- R5: Seed migration's non-Postgres/SQLite fallback wraps the INSERT in
a SAVEPOINT and swallows IntegrityError. Two concurrent migration
runners can no longer race past the SELECT-then-INSERT check.
- R6: IntegrityError tests in test_authz_models.py now verify the
*specific* constraint fired — column names for unique partial indexes
(SQLite doesn't surface the index name), constraint name substring
for CHECK constraints. A generic NOT NULL regression would no longer
pass these tests.
All 180 authz tests pass; ruff clean.
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.
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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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