FlowiseAI

Flowise, an open-source, low-code platform, simplifies the creation and deployment of customized Large Language Model (LLM) applications and AI agents. The platform's drag-and-drop interface makes it accessible to developers with varying levels of coding experience, enabling them to build LLM apps with ease.

Features
- LLM Orchestration: Flowise enables users to connect LLMs with components such as memory, data loaders, cache, and moderation tools. It supports integrations with popular frameworks and tools like LangChain and LlamaIndex, offering over 100 integrations to enhance functionality.
- Agents & Assistants: Users can create autonomous agents that execute various tasks using custom tools, OpenAI Assistant, and Function Agents. These agents can be tailored to specific needs, such as customer support or data querying.
- Developer-Friendly Tools: Flowise provides APIs, SDKs, and embedded widgets to extend and integrate LLM capabilities into existing applications. It supports React SDK for seamless integration into web applications.
- Platform Agnostic: Flowise supports open-source LLMs and can run in air-gapped environments with local LLMs, embeddings, and vector databases. It is compatible with platforms like HuggingFace, Ollama, LocalAI, and Replicate, and supports models such as Llama2, Mistral, Vicuna, Orca, and Llava. Users can self-host on AWS, Azure, and GCP.
- Ready-to-Use Templates: The platform offers ready-to-use app templates that include logic and conditions connected to LangChain and GPT. These templates can be used to quickly build conversational agents with memory, chatbots that interact with PDFs and Excel files, and more.
- Seamless Deployment: Flowise enables seamless deployment on cloud platforms, allowing users to go from testing to production quickly. The low-code approach facilitates rapid iterations and development cycles.

How It Works
1. Installation: Users can install Flowise using npm with the provided commands.
2. Building Applications: Users create applications by dragging and dropping components within the Flowise interface, connecting LLMs to various tools and data sources, configuring logic and conditions, and setting up autonomous agents.
3. Deployment: Once the application is built, it can be deployed on cloud platforms or self-hosted environments.
4. Integration: Developers can extend the functionality of their applications using Flowise's APIs, SDKs, and embedded widgets, allowing for integration with existing systems and workflows.

Integrations
Flowise supports a wide range of integrations, including LangChain, LlamaIndex, HuggingFace, Ollama, LocalAI, Replicate, AWS, Azure, and GCP. These integrations enable users to connect their LLM applications with various data sources, tools, and platforms, enhancing the capabilities and flexibility of their AI solutions.

Generative AI and Foundation Models
Flowise leverages generative AI by integrating with powerful LLMs such as GPT, Llama2, Mistral, Vicuna, Orca, and Llava. The platform's integration with LangChain and other tools allows for sophisticated orchestration of LLMs, enabling the creation of complex AI workflows.

Availability and User Base
Flowise is available as a web app, API, and SDK, making it versatile for different development needs. It is particularly useful for developers looking to build and deploy LLM applications quickly, businesses needing custom AI solutions, and educators and researchers interested in creating educational tools and research assistants.

While Flowise is not open source, it fosters a strong community of developers and users who contribute to its growth and improvement. The platform is backed by YCombinator and trusted by major companies like AWS, Alibaba, Hitachi, ByteDance, Microsoft, InsightSoftware, Google, and Accenture.