ROMA
Permite a criação de sistemas de agentes que executam tarefas complexas de forma confiável e eficiente.
ROMA é uma estrutura open-source para construir sistemas multi-agentes de alto desempenho. Orquestra agentes e ferramentas para resolver problemas complexos através de uma estrutura hierárquica e recursiva.
Mais sobre ROMA
ROMA
ROMA is an open-source meta-agent framework designed for building high-performance multi-agent systems. It structures complex tasks into a hierarchical, recursive tree, facilitating the execution of long-horizon tasks. This framework allows users to create agents that solve intricate problems by orchestrating simpler agents and tools.
- •Hierarchical Task Decomposition:Breaks down complex goals into subtasks using a recursive task tree, improving reliability for multi-step tasks.
- •Transparent Context Flow:Provides fully traceable context flow with structured inputs and outputs, enabling easy debugging and prompt refinement.
- •Modular Agent Integration:Allows users to plug in any agent, tool, or model at the node level, from LLMs to human-in-the-loop checkpoints.
- •Parallel Task Execution:Executes independent sibling nodes in parallel, ensuring high performance for large and demanding problems.
- •State-of-the-Art Performance:ROMA Search achieves 45.6% accuracy on the SEALQA benchmark, outperforming other models.
- •Open-Source and Extensible:Enables anyone to build new agents, extend the framework with custom tools, and adapt it for various domains.
- •Human-in-the-Loop Support:Allows human verification and context addition at any node, especially useful for long-horizon tasks.
- •Stage Tracing for Insights:Offers stage tracing to view inputs and outputs at every node, aiding in error diagnosis and quick iteration.
By choosing ROMA, you gain a robust framework for building advanced AI agents capable of handling complex, multi-step tasks. This open-source solution transforms how agents are designed, offering transparency and flexibility. ROMA empowers you to create innovative solutions, pushing the boundaries of what's possible in the field of AI agents.
