lagent vs ROMA
Python frameworks for LLM agents compared
lagent and ROMA are Python frameworks for building LLM-powered agents that use tools and support synchronous and asynchronous workflows. lagent focuses on connecting agent components and enabling single- or multi-agent patterns, while ROMA centers on recursively decomposing complex tasks and coordinating their execution.
lagent: Build LLM Agents with a PyTorch-Inspired Framework
InternLM’s Lagent is a lightweight Python framework for building LLM-based agents, using a PyTorch-inspired design for composing agents and message passing. It supports synchronous and asynchronous workflows, tools, memory, and multi-agent applications.
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.
| lagent | ROMA | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | — |
| Stars | 2.3k | 5.2k |
| Forks | 248 | 775 |
| Last analyzed | Oct 15, 2026 | Oct 3, 2026 |
Key differences
- lagent provides components for messages, session memory, output parsing, and action execution; ROMA organizes workflows around atomization, planning, execution, and result aggregation.
- lagent supports patterns from a single tool-using agent to multi-agent workflows; ROMA is designed around recursive task decomposition and coordinated agents.
- ROMA includes toolkits for files, calculations, code execution, search, crypto data, and MCP connections; lagent examples cover code execution, browsing, and iterative writing workflows.
- ROMA offers optional REST API, CLI, persistence, storage, and observability components, with a Docker setup for additional services; lagent describes model-serving and tool configuration for chosen workflows.
- ROMA is marked Beta; lagent is not archived.
- lagent lists an Apache-2.0 license; ROMA's supplied project facts do not include a license.
Choose lagent if you…
- need component-oriented control over messages, session memory, parsing, and tool execution.
- want to build a single tool-using agent or a multi-agent workflow in Python.
- are prototyping code execution, browsing, or iterative writing workflows and can configure the required backends and safeguards.
Choose ROMA if you…
- need recursive planning to split complex goals into subtasks and aggregate their results.
- want configurable executors, prediction strategies, and toolkits for atomic tasks.
- 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.