Langflow

Langflow is a low-code platform for building AI agents and RAG applications visually. It targets developers and AI teams who want to move fast without writing boilerplate, while still having full access to the underlying Python when they need it. The visual canvas lets you wire together models, data sources, and tools by dragging components onto a flow, then deploy that flow as an API with minimal extra work.

The integration library is broad:

• LLMs: OpenAI, Anthropic, Mistral, Meta (Llama), Groq, NVIDIA, Amazon Bedrock, Ollama, HuggingFace, and more
• Vector stores: Pinecone, Milvus, Weaviate, Qdrant, Cassandra, Couchbase, Upstash, Vectara
• Data sources: Google Drive, Confluence, Notion, Gmail, GitHub, Slack, MongoDB, and dozens more
• Agent frameworks: LangChain, CrewAI, Composio

Flows are reusable and shareable. You can pick from a library of pre-built components, swap models mid-flow to compare outputs, and run single agents or coordinated fleets where each agent has access to your components as tools. Every flow exposes itself as an API endpoint, so connecting Langflow to an existing product is straightforward.

Python is always available under the hood. Any component can be customized with code, so the visual layer doesn't box you in. Teams that need observability can pair it with tools like Langfuse for tracing.

Deployment is flexible. Self-host it on your own infrastructure, or use the managed cloud option, which runs the same codebase as the open-source version. That parity matters: there's no feature gap to worry about when moving between environments.

Compared to alternatives like Flowise AI or Dify, Langflow leans into Python extensibility and a wide integration surface, making it a practical fit for teams building production-grade agents rather than quick demos.