d1cb8c22a8
Two schema regressions surfaced in QA across the content-blocks chain:
1. MessageResponse.timestamp was typed as a bare datetime, but
Message.timestamp default is a string with microsecond precision and
a UTC timezone label ('%Y-%m-%d %H:%M:%S.%f %Z') that Pydantic's
default datetime parser rejects. Any freshly built Message routed
through MessageResponse.from_message raised ValidationError. Reuse
the shared str_to_timestamp_validator so MessageResponse accepts
every format Message itself recognises.
2. ContentBlock.__init__ marked every field as model_fields_set, not
just the discriminator. The override defeated exclude_unset for the
group content type: a patch like ContentBlock(title='new') dumped
every defaulted field and, when merged onto an existing block by
aupdate_messages, overwrote fields the caller never touched. Mark
only 'type' (the discriminator) so partial updates carry the variant
tag without clobbering the rest.
Adds regression tests in test_message_content_blocks.py: from_message
round-trips Message.timestamp without crashing, and ContentBlock
exclude_unset stays narrow to the explicit fields plus the
discriminator.
(cherry picked from commit 6f6639374f)
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.
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👋 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.