autogen vs ROMA
Multi-agent AI frameworks compared
autogen and ROMA are Python frameworks for building multi-agent AI applications that coordinate language models, tools, and agents. autogen offers layered APIs and common agent interaction patterns, while ROMA centers on recursively decomposing tasks and coordinating their execution and results.
autogen: Build Multi-Agent AI Applications
AutoGen is a Python framework for building AI agents that work independently or alongside people. It supports multi-agent orchestration, model and tool integrations, and prototyping, but is now in maintenance mode.
ROMA: Build Hierarchical Multi-Agent Systems
ROMA is a Python framework for solving complex tasks through recursive planning and coordinated agents. It suits developers building extensible multi-agent workflows with LLMs, tools, and optional persistence or API services.
| autogen | ROMA | |
|---|---|---|
| Language | Python | Python |
| License | CC-BY-4.0 | — |
| Stars | 61.3k | 5.2k |
| Forks | 9.3k | 775 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- autogen provides Core, AgentChat, and Extensions APIs, plus Studio for prototyping and Bench for evaluation; ROMA organizes work around an atomizer, planner, executor, and aggregator, with optional verification.
- ROMA recursively breaks complex goals into subtasks and supports task dependencies; autogen supports event-driven agents and higher-level multi-agent interaction patterns.
- ROMA is marked Beta, while autogen is in maintenance mode and limited to bug fixes, security patches, and documentation improvements.
- ROMA offers optional API, persistence, storage, and observability components; autogen Studio is intended for prototyping and demonstrations, not production deployment.
- autogen lists a CC-BY-4.0 license; ROMA's supplied project details describe it as a Python framework.
Choose autogen if you…
- maintain or migrate an existing application built with AutoGen APIs.
- want to prototype agent interactions with AutoGen Studio or evaluate agent performance with AutoGen Bench.
- need event-driven agents, higher-level chat patterns, or integrations such as MCP workbench support.
Choose ROMA if you…
- need recursive task decomposition with planned subtasks and aggregated results.
- want to configure execution modules, prediction strategies, and callable tools for a structured workflow.
- need optional API, persistence, or observability components for a deployment.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.