4d3e1458da* Update mcp_sse.py Uses Python's built-in asyncio.timeout() context manager Properly handles timeout exceptions Maintains the same functionality but with correct async context management * [autofix.ci] apply automated fixes * add asyncio +clean up comment * [autofix.ci] apply automated fixes * missing arg_schema in Tool Missing args_schema inside cause that tools are generated without input schema and are not able to be properly executed as agent know tool, but dost know what input field tool have. Same problem looks to be in MCP STDIO. * fix Ruff Check Line 56: Error: B904 Within an `except` clause, raise exceptions with `raise ... from err` or `raise ... from None` to distinguish them from errors in exception handling TRY003 Avoid specifying long messages outside the exception class EM102 Exception must not use an f-string literal, assign to variable first * [autofix.ci] apply automated fixes * remove asyncio.timeout Remove asyncio.timeout() (not valid for Py3.10) and replace it by asyncio.wait_for() * [autofix.ci] apply automated fixes * Ruff (TRY300) Move return response.tools inside an else block. This makes it clearer that tools are returned only if the connection is successful, and not if a TimeoutError occurs. * fix: add session initialization check in MCPSseClient Added a check to ensure the session is initialized before attempting to list tools, raising a ValueError with a descriptive message if the session is None. This improves error handling and robustness of the MCPSseClient class. * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Sebastián Estévez <estevezsebastian@gmail.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> |
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Langflow is a powerful tool for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and a built-in API server that turns every agent into an API endpoint 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 to get started quickly and iterate.
- Access to Code so developers can tweak any component using Python.
- Playground to immediately test and iterate on their flows with step-by-step control.
- Multi-agent orchestration and conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
⚡️ Quickstart
Langflow works with Python 3.10 to 3.13.
Install with uv (recommended)
uv pip install langflow
Install with pip
pip install langflow
📦 Deployment
Self-managed
Langflow is completely open source and you can deploy it to all major deployment clouds. Follow this guide to learn how to use Docker to deploy Langflow.
Fully-managed by DataStax
DataStax Langflow is a full-managed environment with zero setup. Developers can sign up for a free account to get started.
⭐ 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.