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* Add OpenSearch multimodal multi-embedding component Introduces OpenSearchVectorStoreComponentMultimodalMultiEmbedding, supporting multi-model hybrid semantic and keyword search with dynamic vector fields, parallel embedding generation, advanced filtering, and flexible authentication. Enables ingestion and search across multiple embedding models in OpenSearch, with robust index management and UI configuration handling. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes * Add EmbeddingsWithModels and sync model fetching Introduces EmbeddingsWithModels class for wrapping embeddings and available models. Updates EmbeddingModelComponent to provide available model lists for OpenAI, Ollama, and IBM watsonx.ai providers, including synchronous Ollama model fetching using httpx. Updates starter project and component index metadata to reflect new dependencies and code changes. * Refactor embedding model component to use async Ollama model fetch Updated the EmbeddingModelComponent to fetch Ollama models asynchronously using await get_ollama_models instead of a synchronous httpx call. Removed httpx from dependencies in Nvidia Remix starter project and updated related metadata. This change improves consistency and reliability when fetching available models for the Ollama provider. * update to embeddings to support multiple models * Add Notion integration components Added several Notion-related components to the component index, including AddContentToPage, NotionDatabaseProperties, NotionListPages, NotionPageContent, NotionPageCreator, NotionPageUpdate, and NotionSearch. These components enable interaction with Notion databases and pages, such as querying, updating, creating, and retrieving content. * Add tests for multi-model embeddings and OpenSearch Added unit tests for EmbeddingsWithModels class and OpenSearchVectorStoreComponentMultimodalMultiEmbedding, including model normalization, authentication modes, and integration scenarios. Updated embedding model component tests to support async build_embeddings and verify multi-model support. Created necessary test package __init__.py files. * Update component_index.json * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * Fix session_id handling in ChatInput and ChatOutput Updated ChatInput and ChatOutput components in starter project JSONs to use the session_id from the graph if not provided, ensuring consistent session management. This change improves message storage and retrieval logic for chat flows. * Update test_opensearch_multimodal.py * [autofix.ci] apply automated fixes --------- 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 3.10–3.13 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.
Caution
- Langflow versions 1.6.0 through 1.6.3 have a critical bug where
.envfiles are not read, potentially causing security vulnerabilities. DO NOT upgrade to these versions if you use.envfiles for configuration. Instead, upgrade to 1.6.4, which includes a fix for this bug.- Windows users of Langflow Desktop should not use the in-app update feature to upgrade to Langflow version 1.6.0. For upgrade instructions, see Windows Desktop update issue.
- Users must update to Langflow >= 1.3 to protect against CVE-2025-3248
- Users must update to Langflow >= 1.5.1 to protect against CVE-2025-57760
For security information, see our Security Policy and Security Advisories.
🚀 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.