In recent times, there was a rise in the expansion of custom large language models (LLMs) applications, which leverage these models for user-specific tasks like retrieval-augmented generation and in-context learning. Multiple platforms have also been developed to assist create such applications, and Flowise is one such open-source platform that permits users to construct customized LLM orchestration flow and AI agents.

Developing LLM tasks could be a complex process, requiring technical knowledge and countless iterations. However, with Flowise, this process becomes far more manageable. Flowise offers a no-code and drag-and-drop approach, empowering users to swiftly transition from testing to production. With quite a lot of nodes like vector embeddings, vector stores, web scrapers, and LLM chains, users can easily connect these nodes to define the appliance’s flow and convey their ideas to life.


To use Flowise, users must have NodeJS as a prerequisite. Subsequently, they will simply install Flowise locally using the command and begin the identical using A link might be provided after that, and users can access the tool by clicking on it. Users can then start constructing AI applications by clicking on the ” button within the top-right corner.


Some use cases of Flowise

  • Users can create a product catalog chatbot that answers any questions related to the products.
  • Users can leverage Flowise to question a SQL database using natural language. They can achieve this by utilizing a custom JavaScript function node within the chat flow to process the input query.
  • Users may use the tool to create follow-up tasks from customer support tickets, enhancing the user experience.

Advantages of Flowise

  • Using Flowise, users can connect LLMs with data loaders, memory, moderations, etc. The tool supports integrations with greater than 100 frameworks, including Langchain and LlamaIndex.
  • Users can create autonomous agents that leverage external tools to execute various tasks.
  • Flowise provides tools like OpenAI Assistant, Function Agent, and APIs that allow users to customize their applications further.
  • Flowise is platform agnostic, and users have the flexibility to make use of models like Llama2, Mistral, and others available on HuggingFace.
  • Flowise can also be developer-friendly, and users can integrate their chat flows with external applications using API endpoints.
  • Flowise will be deployed to quite a few cloud services comparable to AWS, Azure, Render, GCP, etc.

Limitations of Flowise

  • The tool has limited documentation for advanced use cases.
  • It is technical to establish among the cloud providers, comparable to AWS and GCP. 
  • Although the tool doesn’t require any coding language, some basic knowledge is required to integrate chat flows with external applications. Moreover, an understanding of AI concepts can also be mandatory for optimal utilization.
  • The tool could have a learning curve for users who’re latest to low-code development.

In the era of LLMs, there was an increase within the demand for flexible tools that help users create powerful AI applications, even without prior coding knowledge. Flowise has emerged as a promising open-source low-code tool for a similar. It is platform agnostic, developer-friendly, and will be integrated with external applications as well to create unique applications which may truly enhance the best way we leverage AI.

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