OpenAGI vs smolagents
Side-by-side comparison of two AI agent tools
Short answer
- OpenAGI has had no commit in 22 months; smolagents is actively maintained (10 commits in the last 90 days).
- smolagents is growing faster: +531 GitHub stars in the last 30 days vs +5 for OpenAGI.
- Pick OpenAGI for: openAGI: When LLM Meets Domain Experts. Pick smolagents for: smolagents: a barebones library for agents that think in code.
From GitHub data refreshed daily.
OpenAGIopen-source
OpenAGI: When LLM Meets Domain Experts
smolagentsopen-source
π€ smolagents: a barebones library for agents that think in code.
Metrics
| OpenAGI | smolagents | |
|---|---|---|
| Stars | 2.3k | 29.7k |
| Star velocity /mo | 5.210526315789474 | 531 |
| Commits (90d) | 0 | 10 |
| Releases (6m) | 0 | 2 |
| Downloads (30d, npm + PyPI) | 62 | β |
| Overall score | 0.1825190616086469 | 0.625603384872754 |
Pros
- +Research-backed framework with peer-reviewed methodology published in NeurIPS 2023
- +Structured agent sharing ecosystem with upload/download functionality for community collaboration
- +Built-in external tool integration system allowing agents to leverage specialized capabilities
- +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
- -Requires migration to Cerebrum SDK for full AIOS integration, suggesting the main package may have limited standalone utility
- -Rigid folder structure requirements that may limit flexibility in agent organization
- -Heavy dependency on AIOS ecosystem for optimal functionality
- -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 domain-specific expert agents for AIOS deployment in specialized fields like research or analysis
- β’Creating and sharing custom AI agents with the research community through the built-in marketplace
- β’Developing modular agents that leverage external tools for complex multi-step workflows
- 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, OpenAGI or smolagents?
- smolagents has more GitHub stars (29,662 vs 2,287).
- Which is more actively developed, OpenAGI or smolagents?
- smolagents had more commits in the last 90 days (10 vs 0).
- Should I use OpenAGI 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.