FlowiseAI
Flowise is an open-source, low-code tool designed for developers to easily build customized LLM (Large Language Model) apps and AI agents. The platform enables quick iteration, allowing developers to swiftly transition from testing to production.
Flowise offers various features, including LLM Orchestration, which allows for the integration of LLMs with functionalities such as memory, data loaders, cache, and moderation. It also supports the creation of autonomous agents that can execute different tasks using customized tools.
Flowise provides a developer-friendly environment with API, SDK, and embed capabilities. Users can run these applications in different environments, including air-gapped setups with local LLMs, and deploy them on major cloud platforms like AWS, Microsoft Azure, and Google Cloud.
The platform supports a range of use cases, from building product catalogs and customer support chatbots to querying SQL databases and handling structured data. Its no-code/low-code approach democratizes the creation of sophisticated AI-based solutions, making it accessible even to those with minimal coding experience.
Flowise is backed by a vibrant community that contributes to its development and supports new users. Furthermore, it is popular among developers globally for its ability to rapidly build, test, and deploy LLM applications — and its open-source nature has garnered significant appreciation, as demonstrated by its trending on GitHub.
Video Overview ▶️
What are the key features? ⭐
- Drag-and-Drop UI: Build custom LLM flows visually without writing extensive code.
- Multi-Agent Systems: Create coordinated agent workflows with orchestration capabilities.
- RAG Support: Implement retrieval-augmented generation using various data sources and vector stores.
- Human-in-the-Loop: Insert human review steps into agent processes for oversight.
- Observability & Tracing: Full execution traces with support for Prometheus and OpenTelemetry.
Who is it for? 🤔
Examples of what you can use it for 💭
- Startup founder: Quickly builds an AI customer support chatbot that answers questions from product docs using RAG.
- Developer: Creates a multi-agent research assistant where agents handle different tasks like searching, summarizing, and verifying information.
- Content team: Develops an internal tool that queries company knowledge base in natural language and provides accurate responses.
- Product manager: Prototypes conversational AI features for a web app with embedded chat widgets and tool integrations.
- Enterprise IT: Deploys self-hosted AI workflows for secure, scalable document analysis without relying on external cloud limits.
Pros & Cons ⚖️
- Intuitive visual builder
- Strong RAG and agent support
- Fast prototyping
- Learning curve for advanced nodes
- Limited free tier quotas
