Langflow
Visual drag-and-drop builder for LangChain AI agents
- Category
- Coding & Development
- Pricing
- Free and open-source (self-hosted); free Cloud sandbox tier; usage-based production hosting; Enterprise/Premier Support at custom pricing
- Best for
- Langflow is best suited to developers and AI engineering teams who want LangChain's power without hand-writing every chain, and to technical product teams who need to prototype RAG pipelines or multi-agent workflows quickly and then deploy them as APIs.
- Official site
- www.langflow.org
- Last updated
- August 2026
Langflow is an open-source, low-code visual builder for creating AI agents, retrieval-augmented generation (RAG) pipelines, and other LangChain-based applications. It pairs a drag-and-drop React Flow-based canvas with a FastAPI backend, so users connect nodes representing prompts, LLMs, tools, and data sources instead of writing chain logic by hand. Each component is fundamentally a LangChain building block or API wrapper, and the library covers hundreds of options spanning databases, vector stores, web search and scraping, document loaders, OCR, and text splitting. Every flow can also be edited at the code level in Python, so the tool functions as a hybrid between a no-code canvas and a full development environment rather than a rigid template builder.
Langflow began as an open-source "user interface for LangChain" and grew into a broader agent and RAG development platform. It was acquired by DataStax in 2025, and DataStax itself was subsequently acquired by IBM, making Langflow an IBM-backed project while remaining MIT-licensed and open source. The project has a large open-source following on GitHub and Discord, and is used by product and engineering teams at companies like BetterUp, WinWeb, and Athena Intelligence to prototype and ship agent workflows. Its key differentiator versus purely no-code alternatives is that every flow can be deployed immediately as a REST API, an OpenAI-compatible endpoint, or a Model Context Protocol (MCP) server, letting a visual prototype become production infrastructure without a rewrite. DataStax's own hosted Langflow offering is being deprecated in 2026, but the core open-source project and its independent Langflow Cloud continue to be actively developed.
Langflow is best suited to developers and AI engineering teams who want LangChain's power without hand-writing every chain, and to technical product teams who need to prototype RAG pipelines or multi-agent workflows quickly and then deploy them as APIs. It's a weaker fit for non-technical users seeking a purely no-code experience, or for teams that want predictable flat-rate SaaS pricing rather than usage-based cloud costs.
Key features
Visual Flow Builder
A drag-and-drop canvas (built on React Flow) lets users wire together prompts, LLMs, tools, and data sources without writing LangChain boilerplate code.
Component Library
Hundreds of pre-built nodes cover LLM providers, vector databases, web search/scraping, document loaders, OCR, and text splitters, covering most building blocks needed for an agent or RAG pipeline.
Python Escape Hatch
Any component can be opened and edited as raw Python, so users aren't limited to what the visual canvas can express when they need custom logic.
Instant API / MCP Deployment
Every flow automatically exposes as a REST API, an OpenAI-compatible Responses endpoint, or an MCP server, making it easy to embed inside another product.
Multi-Agent Orchestration
Build a single agent or a fleet of agents that share access to the same components as callable tools.
Template Library
Dozens of ready-made flow templates cover common use cases like sales proposal generation, debt collection automation, and enterprise agent workflows.
Self-Hosted or Cloud
The same Langflow experience runs whether self-hosted on-prem, run locally as a desktop app, or deployed on a managed cloud account.
Broad Ecosystem Integrations
Native connectors span OpenAI, Anthropic, Azure, Google Cloud, AWS Bedrock, MongoDB, Pinecone, Notion, Slack, Hugging Face, and dozens of other tools and data sources.
Pricing breakdown
Open Source (Self-Hosted)
- Unlimited flows and components
- Full component library
- Python customization at any node
- Community support via GitHub and Discord
Cloud Free Tier
- Hosted environment to build/test flows without installing anything
- Limited compute and storage for prototyping
- Same UI as self-hosted Langflow
Cloud Production
- Scalable hosted deployment for production agents and APIs
- Database connections for persistent flows
- Enterprise-grade secure cloud infrastructure
Enterprise / Premier Support
- IBM Elite Support (Premier Support) with SLAs
- Dedicated engineering access
- Professional services for bespoke deployments
Pros and cons
Pros
- Completely free open-source core with an MIT license, so teams can self-host with zero licensing cost and full control over their data and infrastructure.
- The component library spans hundreds of pre-built nodes (LLMs, vector databases, scrapers, OCR, data processing), cutting most of the boilerplate typically needed to wire up a RAG or agent pipeline by hand.
- Because every component is ultimately a LangChain building block, users can drop into raw Python at any node for logic the visual canvas alone can't express.
- Flows can become a callable REST API, an OpenAI-compatible endpoint, or an MCP server with one click, making it easy to embed a Langflow flow inside another product or agent.
- Backed by IBM (via the 2025 DataStax acquisition) and a very large open-source community, giving the roadmap real engineering resources behind it.
- Works identically whether self-hosted, run locally as a desktop app, or deployed on the managed cloud, so teams aren't locked into a single deployment model.
Cons
- There's no conventional SaaS pricing page with clear flat tiers; real-world production costs (cloud hosting, LLM API usage, vector DB) can range from roughly $30/month for a hobby project to $2,000+/month for an enterprise deployment, making budgeting less predictable than a fixed subscription.
- DataStax's hosted Langflow offering is being deprecated (shutdown scheduled for April 2026), which has created some uncertainty around the managed-cloud roadmap even though the core open-source project is unaffected.
- Complex multi-agent flows with heavy branching logic and many components can become visually cluttered and harder to debug than the equivalent hand-written LangChain code.
- As with most low-code AI builders, non-developers may still need to touch Python for advanced customization, so it isn't a fully no-code experience for serious production use cases.
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Compare →Frequently asked questions
Is Langflow free?
Yes — Langflow's open-source core is free and MIT-licensed, and there's also a free Cloud sandbox tier for hosted prototyping. Production cloud hosting is billed on usage (compute, API calls, storage).
Who owns Langflow?
Langflow was acquired by DataStax in 2025, and DataStax itself was acquired by IBM (deal closed May 2025), making Langflow an IBM-backed but still MIT-licensed open-source project.
Do I need to know Python to use Langflow?
No for basic flows — the visual drag-and-drop builder covers most common use cases — but Python is available at any node for advanced customization.
Can I deploy a Langflow flow as an API?
Yes, every flow can automatically be exposed as a REST API, an OpenAI-compatible Responses endpoint, or an MCP server.
What LLMs and vector databases does Langflow support?
It integrates with all major LLM providers (OpenAI, Anthropic, Google, Groq, Mistral, Ollama, and more) and vector stores (Pinecone, Milvus, Weaviate, Qdrant, MongoDB, Cassandra, and others).
What happened to DataStax Langflow?
DataStax's hosted Langflow offering was deprecated as of March 2026 with a shutdown scheduled for April 2026; the core open-source Langflow project is unaffected and continues to be developed.
Ready to try Langflow?
Head to the official site to explore pricing and start a free trial where available.
Visit Langflow →