Langfuse vs smolagents
Side-by-side comparison of two AI agent tools
Short answer
- Langfuse is growing faster: +1,812 GitHub stars in the last 30 days vs +531 for smolagents.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick smolagents for: smolagents: a barebones library for agents that think in code.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
smolagentsopen-source
π€ smolagents: a barebones library for agents that think in code.
Metrics
| Langfuse | smolagents | |
|---|---|---|
| Stars | 35.3k | 29.6k |
| Star velocity /mo | 1.8k | 531.1111111111111 |
| Commits (90d) | 2.0k | 10 |
| Releases (6m) | 10 | 2 |
| Overall score | 0.9067292616632036 | 0.6512936293713799 |
Pros
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
- +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
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
- -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’Production LLM application monitoring to track performance, costs, and identify issues in real-time
- β’Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- β’LLM evaluation and testing to measure model performance across different datasets and 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, Langfuse or smolagents?
- Langfuse has more GitHub stars (35,301 vs 29,645).
- Which is more actively developed, Langfuse or smolagents?
- Langfuse had more commits in the last 90 days (2,007 vs 10).
- Should I use Langfuse 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.