c721fcafc8
* fix: guard event serialization against unserializable component payloads (#12591) Loop flows containing a vector store (e.g. Chroma) failed to build with: Error building Component Loop: [TypeError("'_thread.lock' object is not iterable"), TypeError('vars() argument must have __dict__ attribute')] The Loop runs its body in an isolated subgraph and streams an on_end_vertex event for every completed inner vertex. EventManager.send_event serialized that payload with a raw fastapi.jsonable_encoder call. When a payload carries an object jsonable_encoder cannot serialize -- most notably a threading.Lock held by a vector-DB client -- its last-resort fallback raises ValueError([TypeError, TypeError]), which propagated out of the subgraph and aborted the whole flow build. The earlier fix (#12877) only guarded ResultData.validate_model and build_output_logs (both already routed through the fail-safe serialize()), so this jsonable_encoder path was never covered and the issue persisted. send_event now falls back to lfx's fail-safe serialize() when jsonable_encoder raises, degrading unknown objects to a string representation instead of crashing. The happy path is unchanged (the fallback only runs on failure). Adds a focused EventManager unit test and an end-to-end Loop regression test whose body emits a lock-bearing Data (both fail without the fix). * [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.