Langflow

Langflow is free and open-source when you self-host it. The real costs come from the infrastructure and DevOps needed to run it, plus any LLM and vector database usage. A managed cloud option is also available with a free tier and paid plans, so confirm current pricing and limits on the official site before budgeting.

You build AI workflows on a visual canvas by dragging and connecting components such as models, prompts, agents, and data sources. Each node is backed by Python, so you can configure it visually or customize it in code. Once a flow works, you can run it interactively, share it, or expose it as an API endpoint.

Both options are available. You can install and run Langflow yourself via pip for full control over your environment and data, or sign up for the managed cloud to skip infrastructure management. The experience is the same whether you use the open-source version or the cloud.

Langflow supports all major LLM providers and vector databases, including OpenAI, Anthropic, Mistral, Ollama, HuggingFace, Amazon Bedrock, and NVIDIA. It also connects to data sources and stores like Pinecone, Qdrant, Weaviate, Milvus, MongoDB, Notion, Slack, GitHub, and Google Drive, with a growing library of additional components.

Because Langflow can be fully self-hosted, teams can keep their flows and data within their own infrastructure, which is a strong fit for privacy-sensitive use cases. The managed cloud is positioned as an enterprise-grade, secure platform. Review your provider's terms and your own deployment configuration to match your compliance requirements.

Langflow 1.10 is the most recent release highlighted by the project, continuing its focus on agent building and MCP server support. Because the platform iterates quickly, check the changelog on GitHub for the current feature set and breaking changes before upgrading.

Yes, Langflow is free and open-source software. However, you'll incur costs for hosting infrastructure (typically $5-500+/month depending on scale), LLM API usage ($10-1,000+/month based on volume), and optionally vector databases like Pinecone (starting around $50/month for production). Langflow Cloud offers a free tier with limited resources for testing.

Langflow supports all major LLMs and vector databases according to its homepage. It's built on LangChain and can be called from any frontend, backend-for-frontend (BFF), or existing ETL pipeline. Users report integrating it with n8n for broader workflow automation. Specific integration details for individual services were not comprehensively documented in available research.

Compared to Flowise, Langflow is considered more intuitive for AI-specific workflows, though Flowise offers a greater range of components out of the box. Against n8n and Make, Langflow excels at AI agent prototyping but lacks their advanced scheduling and broader business automation capabilities. Users often combine Langflow for AI workflows with n8n for general automation. It's simpler for creating custom Python components than most alternatives.