Langflow is a free, open-source visual builder for LangChain agents and RAG workflows. See 2026 features, self-hosted vs cloud pricing, and top alternatives.
Category
AI
Pricing
Open Source, from Free (self-hosted, MIT license); Langflow Cloud from about $25/month
Langflow is an open-source, low-code visual builder for constructing LangChain-based RAG pipelines and multi-agent AI applications through a drag-and-drop canvas, while still allowing developers to export the underlying Python code.
Originally built by Logspace, founded in 2022, Langflow was acquired by DataStax in 2024 and later became part of IBM through DataStax's 2025 acquisition, while remaining free and open source under the MIT license.
Key Features
Langflow offers a large library of prebuilt, configurable components for LLMs, vector databases, document loaders, memory, and tools that can be visually connected into working AI flows without writing integration boilerplate.
Flows can be tested in a built-in playground, exported as callable API endpoints for embedding into other applications, exported as raw Python code, or composed with other systems using Model Context Protocol (MCP) support.
Pricing
The core Langflow software is entirely free and open source under the MIT license, with self-hosted deployments incurring no licensing cost, only the underlying compute, LLM API, and storage costs a team's flows actually consume.
For teams that prefer managed hosting, Langflow Cloud (via DataStax/IBM) offers a free tier for prototyping, paid plans reported to start around $25 per month, and custom enterprise pricing with dedicated support options.
Key Features
Drag-and-drop flow builder — Visually assemble RAG pipelines and agent workflows by connecting prebuilt components on a canvas.
Prebuilt component library — Includes ready-made nodes for LLM providers, vector databases, document loaders, memory, and tools.
Python code export — Every visual flow can be exported as raw Python code for developers who want to move to hand-tuned production code.
API endpoint generation — Any flow can be published as a callable API endpoint for embedding into external applications.
Multi-agent orchestration — Supports designing workflows where multiple specialized AI agents coordinate to complete a task.
Model Context Protocol support — Flows can expose tools via MCP and be composed with other MCP-compatible AI systems.
Built-in testing playground — Interactively test and debug flows directly inside the visual builder before deploying.
Open-source, self-hosted deployment — Fully free under the MIT license, deployable via Docker or from source on a team's own infrastructure.
Pros & Cons
Pros
Fully open source and free under the MIT license, with no forced paid tier for core functionality
Visual canvas makes RAG and agent prototyping accessible to non-expert developers
Ability to export to raw Python code bridges the gap between prototyping and production
Large and fast-growing GitHub community with over 150,000 stars
Backed by IBM's resources following the DataStax acquisition, aiding long-term maintenance
Cons
Real-world costs still come from LLM API usage, vector databases, and infrastructure, which are not included in the free software
Self-hosted deployments require managing servers, scaling, and monitoring without a managed cloud plan
Visual flows can become difficult to manage for very complex, large-scale agent architectures
Ownership changes (Logspace to DataStax to IBM) may raise questions about long-term product direction for some adopters
Langflow Cloud does not publish a detailed public pricing page beyond a reported starting price
Yes, the core Langflow software is free and open source under the MIT license with no usage restrictions. Langflow Cloud is a separate managed hosting option with free and paid tiers.
Who owns Langflow now?
Langflow was created by Logspace, acquired by DataStax in 2024, and became part of IBM when IBM's acquisition of DataStax closed in May 2025, while remaining open source.
Does Langflow require coding knowledge?
No, Langflow's drag-and-drop canvas lets users build RAG and agent workflows visually, though it also supports exporting flows as Python code for developers who want more control.
What is Langflow used for?
Langflow is used to build retrieval-augmented generation pipelines and multi-agent AI applications visually, using components built around the LangChain framework.
Can Langflow be self-hosted?
Yes, Langflow can be self-hosted for free via Docker or run from source, with no licensing cost since it is fully open source.
What are the main alternatives to Langflow?
Common alternatives include Flowise, n8n, Dify, and LangChain's own code-first framework and LangGraph.