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## Overview Flowise is an open-source, low-code UI for visually building and deploying custom Large Language Model (LLM) applications. It simplifies creating AI agents, chatbots, and Retrieval Augmented Generation (RAG) workflows via a drag-and-drop interface, abstracting complex Langchain implementations. ## Key Features - **Built** with an intuitive drag-and-drop visual canvas for designing sophisticated LLM flows without extensive coding. - **Supports** seamless integration with leading LLM providers like OpenAI (ChatGPT) and Hugging Face. - **Provides** native LangchainJS compatibility, leveraging its vast ecosystem of tools, agents, and chains. - **Enables** advanced RAG pipelines by connecting vector stores (e.g., Pinecone, Chroma) and diverse document loaders. - **Integrates** with chat platforms and APIs for straightforward deployment of custom AI agents. ## Technical Stack - Frontend: React powers the responsive UI. - Backend: TypeScript and JavaScript ensure robust services. - Core Logic: LangchainJS for LLM orchestration. ## Use Cases - Developing custom AI chatbots for customer support or knowledge bases. - Automating business processes via multi-agent workflows. - Building personalized content generation tools using RAG. ## Call to Action Explore Flowise to visually build, test, and deploy powerful AI applications today. Join our open-source community!

Activepieces is an open-source automation framework designed for building agentic workflows and AI-driven business processes. It provides a visual interface for developers to connect over 100 applications with large language models and external data sources. * Implements the Model Context Protocol (MCP) to bridge AI agents with external tools and databases. * Supports custom TypeScript code snippets for executing complex logic within visual workflows. * Enables Retrieval-Augmented Generation (RAG) through native integrations with vector databases. * Provides a self-hostable Docker environment for maintaining data privacy and local execution. * Automates multi-step agentic tasks using conditional branching and error handling. * Built on a TypeScript and Node.js stack with a modular architecture for custom connectors. * Requires a PostgreSQL database and Redis for task queuing and persistence. * Developing automated customer support agents that query internal documentation via RAG. * Orchestrating AI-powered content distribution pipelines across multiple social API endpoints. Deploy Activepieces via Docker to begin building automated agentic workflows on your own infrastructure.