CrewAI
Multi-agent automation framework & studio to build and run AI crews
smolagents
Run sandboxed code agents with minimal code and any LLM
smolagents lets developers create and execute code‑driven agents in a few lines, supporting any LLM, multimodal inputs, and secure sandbox runtimes such as Docker, E2B, Modal, or WebAssembly.
Overview
Overview
smolagents is a lightweight library that lets you build powerful AI agents with just a few lines of Python. Designed for developers and researchers, it abstracts away complex orchestration while keeping the core logic under ~1,000 lines, making the code easy to read and modify.
Capabilities & Deployment
The library ships a first‑class CodeAgent that writes its actions as Python code and runs them in isolated sandboxes—Docker, E2B, Modal, or Pyodide+Deno—so generated code never compromises the host. It is model‑agnostic, supporting any LLM via LiteLLM, HuggingFace Hub, OpenAI‑compatible APIs, local Transformers, Azure, Bedrock, and more. Agents accept text, vision, video, and audio inputs and can call tools from the HuggingFace Hub, LangChain, or custom MCP servers. A simple CLI (smolagent and webagent) enables quick experimentation without writing extra boilerplate.
Highlights
Minimal code footprint (~1,000 lines) for full agent logic
CodeAgent writes actions as Python and executes in secure sandboxes (Docker, E2B, Modal, Pyodide+Deno)
Model‑agnostic architecture works with OpenAI, Anthropic, Together, HuggingFace, local Transformers, Azure, Bedrock, etc.
Modality‑agnostic support for text, vision, video, and audio with Hub sharing for agents and tools
Pros
- Simple API lets you spin up agents with a single import
- Broad LLM compatibility avoids vendor lock‑in
- Secure execution options protect host environments
- Community‑driven Hub sharing accelerates reuse
Considerations
- Sandbox setup may require external services (E2B, Modal) or Docker
- Advanced multimodal pipelines can add complexity
- Limited to Python code actions; non‑Python environments need wrappers
- Documentation is still evolving; some features lack extensive examples
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Fit guide
Great for
- Rapid prototyping of AI assistants that need to run code
- Teams that want to experiment with multiple LLM providers
- Projects requiring secure execution of generated code
- Developers looking to share reusable agents via the HuggingFace Hub
Not ideal when
- Use cases demanding native non‑Python runtimes
- Environments without Docker or internet access to sandbox services
- Scenarios where ultra‑low latency is critical and sandbox overhead is unacceptable
- Users needing a full‑featured orchestration platform beyond single‑agent workflows
How teams use it
Automated data analysis report
Agent fetches datasets, writes pandas scripts, executes them in Docker, and returns a formatted summary.
Web‑based product price scraper
Webagent navigates e‑commerce pages, extracts product details, and delivers price information in seconds.
Multimodal image captioning
Agent receives an image, calls a vision model, generates descriptive text, and stores results in a Hub Space.
Cross‑provider LLM benchmarking
Runs identical prompts across OpenAI, Anthropic, and local models, collecting latency and quality metrics for comparison.
Tech snapshot
Python100%
Makefile1%
Frequently asked questions
How does smolagents ensure the safety of generated code?
CodeAgent executes actions inside isolated sandboxes such as Docker, E2B, Modal, or Pyodide+Deno, preventing arbitrary code from affecting the host system.
Can I use a locally hosted model?
Yes, the TransformersModel wrapper lets you load any HuggingFace model on your hardware and use it as the agent’s LLM.
What tools can I attach to an agent?
Agents accept any callable tool, including HuggingFace Hub spaces, LangChain utilities, or custom functions exposed via MCP servers.
Is there a command‑line interface?
Two CLI commands are provided: `smolagent` for general multi‑step agents and `webagent` for focused web‑browsing tasks.
Do I need to install extra dependencies for sandboxing?
Sandbox backends are optional; installing the `[toolkit]` extra pulls common tools, and you can add Docker, E2B, or Modal SDKs as needed.
Project at a glance
Active- Stars
- 25,026
- Watchers
- 25,026
- Forks
- 2,257
LicenseApache-2.0
Repo age1 year old
Last commit3 hours ago
Primary languagePython
Last synced 2 hours ago