AutoAgent
Allow non-coders to build and run LLM agents through natural-language dialogue — the framework profiles agents, generates tools and workflows, and runs them in Docker-isolated environments, with architecture drawn from OpenAI Swarm and Magentic-One.
Description
AutoAgent (HKUDS, formerly MetaChain, MIT) is a 'Fully-Automated & Zero-Code LLM Agent Framework'. It exposes three modes: User Mode (a ready-to-use multi-agent research assistant), Agent Editor (natural-language single-agent construction with automated profiling and tool generation), and Workflow Editor (natural-language multi-agent workflow construction without tool creation). Code generation and execution run inside Docker containers, with browser environment integration; design influences are explicitly OpenAI Swarm and Microsoft's Magentic-One three-agent pattern. Evaluated on GAIA and MultiHopRAG.
Solution
Three-mode shell. In User Mode a ready-built multi-agent research assistant runs over the user's task; in Agent Editor the user describes an agent in natural language and AutoAgent profiles it, generates required tools, and emits a runnable agent; in Workflow Editor the user describes a multi-agent workflow and AutoAgent synthesises an executable workflow without authoring new tools. Inside each agent the loop is tool-calling: model picks a function, the function runs inside a Docker container (file system, code execution, browser), the result is fed back. Architectural inspiration is OpenAI Swarm (handoffs) and Magentic-One (lead orchestrator + workers).
Primary use cases
- zero-code single-agent construction via natural language
- natural-language multi-agent workflow construction
- ready-to-use multi-agent research assistant (User Mode)
- GAIA-benchmark style autonomous problem solving
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