LiteLLM vs smolagents
Side-by-side comparison with live GitHub signals. Last updated May 16, 2026.
| Metric | LiteLLM | smolagents |
|---|---|---|
| GitHub Stars | 47.2K | 27.3K |
| Contributors | 385 | 200 |
| Last Commit | May 16, 2026 | May 14, 2026 |
| Open Issues | 3091 | 535 |
| License | open-source | open-source |
| Pricing | open-source | open-source |
| Free Tier | Yes | Yes |
| Category | frameworks | frameworks |
| Trending | No | No |
Shared Tags
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About LiteLLM
LiteLLM provides a unified OpenAI-compatible API for 200+ LLM providers (OpenAI, Anthropic, Google, Azure, AWS Bedrock, Ollama, and more). Use one interface to call any model, with built-in load balancing, fallbacks, spend tracking, and rate limiting. Essential infrastructure for multi-model agent systems.
View full listingAbout smolagents
smolagents is Hugging Face's minimalist agent framework that prioritizes simplicity and transparency over abstraction. At its core are CodeAgent, which writes and executes Python code as actions rather than relying on JSON tool calls, and ToolCallingAgent for traditional function-calling workflows. The framework is model-agnostic and integrates natively with the Hugging Face Hub, Transformers, and Inference API. Its small surface area makes it easy to audit, extend, and debug — ideal for researchers and practitioners who want full visibility into agent behavior without fighting a complex framework.
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