ec0b11d721
* chore: update lock files update lock files * fix: migrate Mongo/Weaviate/Perplexity components off removed langchain-community classes langchain-community 0.4.2 (pulled in by the lock update) removed the MongoDBAtlasVectorSearch and Weaviate vector stores and the ChatPerplexity chat model. The MongoDB import failed at test collection, aborting both backend unit-test groups with an ImportError. Migrate to the standalone packages, all already declared dependencies: - mongodb: langchain_mongodb.MongoDBAtlasVectorSearch (drop-in) - perplexity: langchain_perplexity.ChatPerplexity (drop-in) - weaviate: rewrite for weaviate-client v4 (connect_to_weaviate_cloud / connect_to_custom) + langchain_weaviate.WeaviateVectorStore, adding advanced gRPC host/port inputs. The component was already broken on weaviate-client v4, which removed the v3 weaviate.Client(url=...) API. Component class names and identifiers are unchanged, so existing flows are preserved. The component index is left for the autofix CI job to regenerate. * chore: auto-bake note keys and regenerate backend locales/en.json [skip ci] * ci: wake up CI Co-Authored-By: Oz <oz-agent@warp.dev> * test: fix concurrent-import deadlock in test_all_modules_importable test_all_lfx_component_modules_directly_importable imports ~488 modules concurrently via asyncio.gather over asyncio.to_thread(importlib.import_module). It flaked ~50% with: _DeadlockError: deadlock detected by _ModuleLock('toolguard.runtime.runtime') on lfx.components.models_and_agents.policies.tool_invoker toolguard has an internal circular import: toolguard/runtime/__init__.py does `from .runtime import ...` while runtime.py does `from toolguard.runtime import IToolInvoker`. It resolves fine single-threaded, but the lfx policy modules reach the cycle from two entry points at once -- policies.tool_invoker enters at the toolguard.runtime package while policies.guard_sync_utils enters at the toolguard.runtime.runtime submodule. On separate worker threads one holds the package lock waiting on the submodule lock while the other does the reverse, so CPython's import machinery raises _DeadlockError. Pre-import both entry points single-threaded before the fan-out so sys.modules is warm and the threaded imports only hit the cache. Keeps full parallelism and coverage (every module is still imported; no skip-list entry). Verified 24/24 green vs a 4/8 baseline. * add tests * pragma * Update component_index.json * [autofix.ci] apply automated fixes * test: fix dropdownComponent fixture import for langchain-community 0.4.2 The dropdownComponent Playwright test pastes component code into the code editor and clicks "Check & Save", which validates the code by importing it. langchain-community 0.4.2 (this branch's lock bump) removed `langchain_community.chat_models.bedrock`, so the import threw, the code modal stayed open, and enableInspectPanel timed out clicking `canvas_controls_dropdown_help` through the open dialog. Failed deterministically (all retries + GHA re-run), only on this branch. Swap the fixture to `langchain_aws.ChatBedrock`, matching the real Amazon Bedrock component which already migrated, and upstream guidance (BedrockChat deprecated since lc 0.0.34). Also move the 0.4.2-removed `langchain_community.chat_models.litellm` import off the top level of the deactivated ChatLiteLLM component into build_model so the module imports cleanly. No loaded/product components affected. * [autofix.ci] apply automated fixes * chore: update pandas and numexpr * Update component_index.json * chore: update templates --------- Co-authored-by: Eric Hare <ericrhare@gmail.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Oz <oz-agent@warp.dev> 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.