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* fix(api/v2): address review findings on the v2 workflows endpoint - recover session_id for completed background jobs from the persisted terminal message instead of always returning null, so GET status can continue the same chat/memory thread - replay a user-cancel as a CUSTOM cancel marker + RUN_FINISHED (agui) and a `cancelled` terminal (langflow) instead of RUN_ERROR, so a re-attaching client no longer reads a deliberate stop as a failure - cancel the evicted still-running buffer writer when the background-run registry is full, so it stops appending into a run no reader can find - derive per-component status from the error artifact / valid flag instead of hardcoding COMPLETED, and stop the langflow adapter dropping `valid` - throttle the unauthenticated public endpoint per IP and bound its input_value/session_id length - document the sync-only scope of request-body globals - document that live event re-attach is intentionally owner-only * test(lfx): register public_flow_rate_limit_per_minute in settings composition * refactor(v2 workflows): split workflow.py and address review blockers Splits the ~1.5k-line workflow.py into focused modules and folds in the execution-timeout and error-sanitization fixes from Cristhianzl's review of #13307. - B1: workflow.py now holds only the four route handlers. Validation guards move to workflow_validation, the sync/stream run loop to workflow_execution, and the durable background machinery to workflow_background (layered, acyclic). - I1: add workflow_execution_timeout (default 300) and apply a single wall-clock ceiling across sync, stream, background, and public via _stream_event_frames. A timeout becomes a sanitized terminal error and marks a background job failed. - I3: the route error handlers no longer echo raw exception text. They return a generic, code-tagged message and log the full exception server-side. - R1: remove the "commented out / future scope" comments that sat over live dataframe-extraction code in converters.py. - R4: drop the worker-routing internals from the reattach 409 message. Tests cover the timeout terminal-error path and the error-body sanitization, and the settings field-count guard is updated for the new setting. * refactor(lfx): extract v2 workflow contract layer into lfx.workflow Moves the protocol-agnostic pieces of the v2 workflows API out of the langflow backend into lfx so both the backend and `lfx serve` can share one contract. First step toward giving lfx (the production runtime) the v2 workflows API. - Move api/v2/adapters/, agui_translator.py, and converters.py to lfx/workflow/. They depend only on lfx.schema.workflow and ag_ui (already an lfx dep), so lfx carries the contract with zero langflow imports. - Decouple the one langflow reference: converters typed run_response against langflow.api.v1.schemas.RunResponse (TYPE_CHECKING only). Replaced with a local RunResponseLike Protocol (outputs + session_id), the only attributes used. - Repoint the six backend v2 workflow modules to import from lfx.workflow. - Move the five protocol-agnostic contract tests into src/lfx/tests/unit/workflow/ (run in the lfx-only env). test_output_event_parity and test_workflow_agui stay in langflow (they need langflow.api.build) with repointed imports. Coverage unchanged: 201 contract tests pass in the lfx-only env, 191 backend v2 tests pass; 392 total, same as before the move. * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.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.