d68e4bbe08
* fix: route IBM WatsonX selections to model_id endpoint in unified get_llm A model selection sourced from the GET /api/v1/models catalog (which the frontend uses to augment the Language Model / Agent dropdown right after a provider is configured) carries only raw create_model_metadata fields — none of the enriched *_param keys that get_language_model_options injects. get_llm already derived model_class from the provider mapping for this case but still let model_name_param / api_key_param / url_param / project_id_param fall back to the generic defaults. For IBM WatsonX this is load-bearing: langchain_ibm.ChatWatsonx exposes both a `model` field (routes to the OpenAI-style Model Gateway, a different catalog) and a `model_id` field (routes to ModelInference, the foundation-models endpoint the dropdown is populated from). Falling back to the generic `model` sent the selected foundation-model id to the gateway, failing with "model <id> not found" / IAM "Provided user not found or active" — even though the dropdown, connection test, and standalone IBM watsonx.ai component all work. Derive all param names from get_provider_param_mapping(provider) when the selection metadata lacks them. Only WatsonX (model_id/apikey) differs from the generic defaults, so OpenAI/Anthropic/etc. are unaffected. Fixes #13671 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> 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.
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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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