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Drag & drop UI to build your customized LLM flow
⛓️ Langflow is a visual framework for building multi-agent and RAG applications. It's open-source, Python-powered, fully customizable, model and vector store agnostic.
🤖️ Deploy GPT-4o ChatGPT on your WeChat within 2 steps! 两步在云端部署你的微信ChatGPT聊天机器人！🤖️
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
✨ A beautiful UI for ChatGPT and other conversational models
The UI design language and React library for Conversational UI
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, le…
[AI Agent Application Development Framework] - 🚀 Build AI agent native application in very few code 💬 Easy to interact with AI agent in code using structure data and chained-calls syntax 🧩 Enhance …
A multi-agent framework enabling the development of decentralized, collaborative systems with modular architecture for flexibility and extensibility.
A GPT-based RAG agent that can search 2.4 million ArXiv abstracts to answer all your scientific and technical questions. It has the potential to be more precise and have fewer hallucinations than C…
Build resilient language agents as graphs.
Agent framework and applications built upon Qwen2, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.
Start building LLM-empowered multi-agent applications in an easier way.
OpenResume is a powerful open-source resume builder and resume parser. https://open-resume.com/
OpenAgents: An Open Platform for Language Agents in the Wild
🚀CodiumAI PR-Agent: An AI-Powered 🤖 Tool for Automated Pull Request Analysis, Feedback, Suggestions and More! 💻🔍
An Open-source Framework for Autonomous Language Agents
AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力
Question and Answer based on Anything.
A set of LangChain Tutorials from my youtube channel
AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.