194f42be81
* perf: cache Ollama model capabilities to fix slow Cloud model toggles Enabling/disabling models for an Ollama provider pointed at the cloud base URL (https://ollama.com) lagged on every toggle, while a local Ollama stayed instant (issue #12399). Root cause: get_ollama_models() probes capabilities with one POST /api/show per model on top of the GET /api/tags listing, so a single catalog read costs N + 1 upstream requests. GET /enabled_models reads both llm and embeddings (replace_with_live_models(model_type=None)), so each read paid 2 x (N + 1), and every model toggle triggers a refetch. On Ollama Cloud's large public catalog over the internet this crawls; local Ollama has a tiny catalog and ~0 latency so it stays fast. The existing 30s list cache only helps clustered same-capability reads. Fix: add a per-model capability cache keyed by (base_url, model_name). A model:tag's capabilities are intrinsic to the model, so the /api/show result is reused instead of re-probed. This makes the llm and embedding reads share a single fan-out (N probes, not 2N) and makes any read after the short list-TTL re-probe only models never seen before, not the whole catalog. TTL is bounded at 10 minutes so a re-pull that changes a tag's capability class self-heals quickly. A probe failure is still absorbed and left uncached so one bad model never poisons or sticks in the catalog. Backend-only and behavior-preserving: the model lists returned are identical; only the upstream request count drops. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * perf: avoid caching empty Ollama capabilities and prune stale entries Address review feedback on the per-model capability cache (#13722). A 200 /api/show with no capabilities resolved to [] and was cached for the full TTL, while a real RequestError/HTTPStatusError was correctly left uncached, so a transient empty response could hide a model from the picker for 10 minutes. Cache only a populated capability list, so an empty/absent array is re-probed on the next read like the error path. Real Ollama always returns a populated array, so no legitimately-capable model is ever re-probed. The capability cache expired on read but never evicted, retaining one entry per distinct model seen for the process lifetime. Prune it to the live catalog on each fresh read so it tracks the current catalog; entries for other base URLs are untouched. Adds behavioral tests for both paths. --------- 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.