OmO vs smolagents
Side-by-side comparison of two AI agent tools
Short answer
- OmO is growing faster: +1,005 GitHub stars in the last 30 days vs +531 for smolagents.
- Pick OmO for: omO: Just type "mass ulw" keyword with your prompt. Pick smolagents for: smolagents: a barebones library for agents that think in code.
From GitHub data refreshed daily.
O
OmOopen-source
OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.
smolagentsopen-source
π€ smolagents: a barebones library for agents that think in code.
Metrics
| OmO | smolagents | |
|---|---|---|
| Stars | 69.8k | 29.6k |
| Star velocity /mo | 1.0k | 531.1111111111111 |
| Commits (90d) | 9.4k | 10 |
| Releases (6m) | 10 | 2 |
| Overall score | 0.9105351293499632 | 0.6512936293713799 |
Pros
- +Code-first agent approach provides precise control over agent actions compared to natural language-based systems
- +Extremely lightweight architecture with core logic in ~1,000 lines of code, making it easy to understand and customize
- +Multiple sandboxed execution options ensure secure code execution in production environments
Cons
- -Limited documentation in the provided source, potentially creating learning curve for new users
- -Code-based approach may require more programming knowledge compared to natural language agent frameworks
- -Dependency on external sandbox providers (Blaxel, E2B, Modal) for secure execution may add complexity
Use Cases
- β’Building AI agents that need to perform precise code-based actions like data analysis, file manipulation, or API integrations
- β’Developing secure agent systems where code execution must be isolated in sandboxed environments
- β’Creating shareable agent tools and workflows that can be distributed through the Hugging Face Hub ecosystem
FAQ
- Which is more popular, OmO or smolagents?
- OmO has more GitHub stars (69,754 vs 29,645).
- Which is more actively developed, OmO or smolagents?
- OmO had more commits in the last 90 days (9,367 vs 10).
- Should I use OmO or smolagents?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.