AutoAgentOpen-source zero-code LLM framework to create and deploy multi-agent workflows via natural language.

AutoAgent is a fully-automated and zero-code LLM (Large Language Model) framework that allows for the creation and deployment of multi-agent workflows via natural language. It enables users to effortlessly build ready-to-use tools, agents, and workflows without requiring coding knowledge. The framework is designed to be dynamic, extensible, customized, and lightweight. AutoAgent leverages its native self-managing vector database to outperform industry-leading solutions like LangChain. It supports a wide range of LLMs, including OpenAI, Anthropic, Deepseek, vLLM, Grok, and Huggingface. The framework offers flexible interaction modes, benefiting from support for both function-calling and ReAct interaction modes. One of its key strengths is its agentic-RAG (Agent and Relation-aware Graph) architecture. It has ranked the #1 spot among open-sourced methods on the GAIA benchmark, delivering comparable performance to OpenAI's Deep Research. AutoAgent is a valuable tool for users who need to create and deploy AI-powered workflows without requiring extensive coding expertise. Despite its strengths, AutoAgent's agentic-RAG architecture can be complex, requiring a good understanding of natural language processing and machine learning concepts. Moreover, the framework's flexibility can also make it challenging to manage and integrate with existing tools and systems. AutoAgent's native self-managing vector database can be slow to initialize and may require significant computational resources. Furthermore, the framework's reliance on LLMs can make it prone to performance variability depending on the specific model used. Key features of AutoAgent include its top performance on the GAIA benchmark, agentic-RAG architecture with native self-managing vector database, effortless workflow creation with natural language, universal LLM support, flexible interaction modes, and lightweight design.