LazyLLM vs ROMA
Python frameworks for multi-agent LLM applications compared
LazyLLM and ROMA are Python frameworks for building applications that coordinate LLMs and agents. LazyLLM spans application workflows such as RAG, model tuning, and deployment, while ROMA focuses on recursively decomposing tasks and coordinating their execution.
LazyLLM: Build Multi-Agent LLM Applications
LazyLLM is a Python framework for assembling, testing, and deploying multi-agent LLM applications with low-code workflows. It suits developers who want to combine models, tools, and RAG components while iterating across local or online services.
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.
| LazyLLM | ROMA | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | — |
| Stars | 3.9k | 5.2k |
| Forks | 423 | 775 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- LazyLLM provides composable flows for applications, including pipelines, routing, loops, and RAG; ROMA organizes work around recursive planning, execution, and result aggregation.
- LazyLLM covers model use, retrieval, fine-tuning, interfaces, and deployment; ROMA centers on task orchestration, with optional API, persistence, storage, and observability components.
- LazyLLM supports online services and local models through documented integrations; ROMA supports configurable language models and callable tools, including toolkits for search, files, calculations, and code execution.
- LazyLLM is licensed under Apache-2.0; ROMA's license is not included in the supplied facts.
- ROMA is marked Beta, with APIs and behavior potentially subject to change; LazyLLM is not marked Beta in the supplied information.
- LazyLLM may require additional frameworks and suitable compute for local inference or fine-tuning; ROMA's optional services require extras or its Docker setup.
Choose LazyLLM if you…
- want to combine RAG, model calls, tools, and user interfaces in application workflows.
- need documented options for local or online models, fine-tuning, and deployment.
- are comfortable learning module and flow abstractions for a broader application lifecycle.
Choose ROMA if you…
- need recursive decomposition and coordinated execution for complex tasks.
- want to configure planners, executors, tools, and optional result verification.
- need optional API, persistence, or observability components and can account for ROMA's Beta status.
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.