LLM Agents vs smolagents
Side-by-side comparison of two AI agent tools
Short answer
- LLM Agents has had no commit in 15 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 +2 for LLM Agents.
- Pick LLM Agents for: build agents which are controlled by LLMs. Pick smolagents for: smolagents: a barebones library for agents that think in code.
From GitHub data refreshed daily.
LLM Agentsopen-source
Build agents which are controlled by LLMs
smolagentsopen-source
π€ smolagents: a barebones library for agents that think in code.
Metrics
| LLM Agents | smolagents | |
|---|---|---|
| Stars | 1.1k | 29.7k |
| Star velocity /mo | 2.0526315789473686 | 531 |
| Commits (90d) | 0 | 10 |
| Releases (6m) | 0 | 2 |
| Downloads (30d, npm + PyPI) | 14 | β |
| Overall score | 0.1665593033584882 | 0.625603384872754 |
Pros
- +Educational transparency with minimal abstraction layers for understanding agent mechanics
- +Easy customization and extension with simple tool integration API
- +Lightweight codebase that's easy to modify and debug
- +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 built-in tools compared to comprehensive frameworks like LangChain
- -Requires manual setup of API keys for OpenAI and optional SERPAPI services
- -Lacks advanced features like memory management, conversation history, or production optimizations
- -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’Learning how LLM agents work by studying and modifying a simple implementation
- β’Rapid prototyping of custom agent workflows with specific tool combinations
- β’Building educational demos or simple automation tasks where transparency matters more than features
- 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, LLM Agents or smolagents?
- smolagents has more GitHub stars (29,662 vs 1,055).
- Which is more actively developed, LLM Agents or smolagents?
- smolagents had more commits in the last 90 days (10 vs 0).
- Should I use LLM Agents 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.