AutoAgent is an innovative zero-code and low-code LLM agent framework that lets users create complete intelligent agent systems by describing behavior in natural language. The project has gained wide attention on GitHub and demonstrates strong capabilities in GAIA benchmark tests, offering features comparable to deep research prototypes.
Zero-code agent creation
The framework’s hallmark is its zero-code capability — users can design complex agent systems without writing any code. By describing desired behaviors in natural language, AutoAgent can generate appropriate tools, agents, and workflows, greatly lowering the technical barrier to building AI applications.
Self-managed vector stores
AutoAgent includes built-in self-managed vector store support for Agentic-RAG tasks, demonstrating strong capabilities similar to industry-leading solutions like LangChain. This self-management mechanism enables agents to autonomously maintain and query knowledge without heavy manual intervention.
Runtime trace and learning
The framework supports runtime trace management so agents can learn and adapt from executed runs. Combining ReAct-style workflows and functional hooks, agents can adjust strategies based on actual context, delivering more intelligent and personalized experiences.
Broad model support
AutoAgent supports a wide range of LLMs including OpenAI, Anthropic, DeepSeek, vLLM, Grok, and Hugging Face hosted models. This flexibility helps users select models depending on cost, performance, and functional needs.