AutoResearch-SibylSystem
by Sibyl-Research-Team
Autonomous multi-agent research system with self-evolution and experiment orchestration
Overview
Coordinates fully autonomous research agents that propose, run, and iterate on scientific experiments. Uses Claude Code as the executional LLM and an internal orchestration layer for task delegation, experiment execution, GPU scheduling, and paper generation. Includes self-evolution and self-healing behaviors so agent chains can adapt their strategies and recover from failures. Hierarchical Multi-Agent Pattern and an Agent Protocol underpin how tasks are delegated and coordinated across agent teams.
Why It Matters
Best For
Researchers and teams building automated scientific pipelines who want agents that can plan, execute, and iterate on experiments with built-in self-repair. Model Context Protocol (MCP) Pattern provides a structured way to manage context and coordination across agent plans.
How It's Used
- Automating experimental design, execution, and result summarization for scientific workflows
- Testing agent delegation and recovery by running long-lived experiment pipelines with GPU scheduling
- Evaluating agent reliability and track record in iterative research tasks to inform reputation models