CrewAI
Multi-agent automation framework & studio to build and run AI crews
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
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.
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.
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.
When teams consider smolagents, these hosted platforms usually appear on the same shortlist.
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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.
CodeAgent executes actions inside isolated sandboxes such as Docker, E2B, Modal, or Pyodide+Deno, preventing arbitrary code from affecting the host system.
Yes, the TransformersModel wrapper lets you load any HuggingFace model on your hardware and use it as the agent’s LLM.
Agents accept any callable tool, including HuggingFace Hub spaces, LangChain utilities, or custom functions exposed via MCP servers.
Two CLI commands are provided: `smolagent` for general multi‑step agents and `webagent` for focused web‑browsing tasks.
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
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