ROMA
ROMA (Recursive Open Meta-Agent) is an open-source meta-agent framework that uses recursive hierarchical structures to orchestrate multiple AI agents and tools for solving complex problems with full transparency and state-of-the-art performance.
https://www.sentient.xyz/blog/recursive-open-meta-agent?ref=producthunt
Product Information
Updated:Sep 12, 2025
What is ROMA
ROMA is a groundbreaking meta-agent framework developed by Sentient that serves as a backbone for building high-performance multi-agent systems. It's designed to tackle complex tasks by coordinating multiple specialized agents and tools in a structured, hierarchical manner. As an open-source framework, ROMA represents a significant step toward making advanced AI capabilities more accessible and transparent, allowing developers to build, customize, and extend AI agents for various applications ranging from research analysis to creative content generation.
Key Features of ROMA
ROMA (Recursive Open Meta-Agent) is an open-source meta-agent framework that uses recursive hierarchical structures to solve complex problems. It breaks down tasks into parallelizable components using a tree-like architecture where parent nodes decompose complex goals into subtasks for child nodes to handle. The framework provides full transparency in context flow, supports multiple AI models and tools, and enables developers to build high-performance multi-agent systems while maintaining traceability and easy debugging capabilities.
Recursive Hierarchical Structure: Uses a tree-like architecture where complex tasks are broken down into smaller subtasks, with parent nodes managing context flow between child nodes
Transparent Context Flow: Provides full traceability of decision-making processes and context flow between agents, enabling easy debugging and refinement
Modular Design: Allows integration of any agent, tool, or model at the node level, including specialized LLM-based agents and human-in-the-loop checkpoints
Parallel Processing: Enables simultaneous execution of independent subtasks, making it efficient for handling large-scale complex problems
Use Cases of ROMA
Research and Analysis: Conducting comprehensive research by breaking down complex queries into subtasks, gathering information from multiple sources, and synthesizing findings
Content Creation: Generating creative content like podcasts, comics, and research reports by coordinating multiple specialized agents
Financial Analysis: Processing complex financial data and generating insights by decomposing analysis tasks into manageable components
Software Development: Automating software development pipelines using interconnected agents for different development tasks
Pros
Open-source and fully extensible
High performance on complex tasks through parallel processing
Transparent and traceable decision-making process
Cons
Requires careful planning of task decomposition
May have increased complexity for simple tasks that don't need hierarchical breakdown
How to Use ROMA
Installation: Install ROMA framework from GitHub repository at https://github.com/sentient-agi/ROMA
Environment Setup: Configure environment and dependencies including Python and Pydantic for data validation
Define Task Structure: Create a hierarchical task structure by defining parent and child nodes that will break down your complex goal into subtasks
Configure Node Types: Set up the four main node types: Atomizer (assesses tasks), Planner (decomposes into subtasks), Executor (performs tasks), and Aggregator (combines results)
Add Agents/Tools: Plug in required agents, tools, or models at the node level based on your specific use case needs
Set Context Flow: Define how context and information flows between parent and child nodes using Pydantic inputs/outputs for transparency
Enable Parallelization: Configure independent sibling nodes to run in parallel for better performance on large tasks
Add Verification Steps: Optionally add human-in-the-loop checkpoints or verification steps at key nodes
Run and Monitor: Execute your agent system and use stage tracing to monitor inputs/outputs at every node for debugging
Iterate and Refine: Use the transparent architecture to identify areas for improvement and refine prompts, tools, and verification steps as needed
ROMA FAQs
ROMA (Recursive Open Meta-Agent) is an open-source meta-agent framework that uses recursive hierarchical structures to build high-performance multi-agent systems. It orchestrates simpler agents and tools to solve complex problems through a hierarchical, recursive task tree structure.
ROMA Video
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